U.S. patent application number 17/517496 was filed with the patent office on 2022-02-24 for methods and systems for predicting response to anti-tnf therapies.
The applicant listed for this patent is Scipher Medicine Corporation. Invention is credited to Susan Dina GHIASSIAN, Keith J. JOHNSON.
Application Number | 20220056121 17/517496 |
Document ID | / |
Family ID | |
Filed Date | 2022-02-24 |
United States Patent
Application |
20220056121 |
Kind Code |
A1 |
JOHNSON; Keith J. ; et
al. |
February 24, 2022 |
METHODS AND SYSTEMS FOR PREDICTING RESPONSE TO ANTI-TNF
THERAPIES
Abstract
Methods and systems for administering anti-TNF therapy to
subjects who have been determined to display a gene expression
response signature established to distinguish between responsive
and non-responsive prior subjects who have received the anti-TNF
therapy.
Inventors: |
JOHNSON; Keith J.; (Wayland,
MA) ; GHIASSIAN; Susan Dina; (Boston, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Scipher Medicine Corporation |
Waltham |
MA |
US |
|
|
Appl. No.: |
17/517496 |
Filed: |
November 2, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16394046 |
Apr 25, 2019 |
11198727 |
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17517496 |
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PCT/US19/22588 |
Mar 15, 2019 |
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16394046 |
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62644070 |
Mar 16, 2018 |
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International
Class: |
C07K 16/24 20060101
C07K016/24; G16H 50/30 20060101 G16H050/30; A61P 19/02 20060101
A61P019/02; C12Q 1/6827 20060101 C12Q001/6827; G16B 20/20 20060101
G16B020/20; G16B 40/30 20060101 G16B040/30; G16B 45/00 20060101
G16B045/00; C12Q 1/6851 20060101 C12Q001/6851; C12Q 1/6883 20060101
C12Q001/6883 |
Claims
1. A method of treating subjects with anti-TNF therapy, the method
comprising a step of: administering the anti-TNF therapy to
subjects who have been determined to display a gene expression
response signature established to distinguish between responsive
and non-responsive prior subjects who have received the anti-TNF
therapy.
2. The method of claim 1, wherein the responsive and non-responsive
prior subjects suffered from the same disease, disorder, or
condition.
3. The method of claim 2, wherein the subjects to whom the anti-TNF
therapy is administered are suffering from the same disease,
disorder or condition as the prior responsive and non-responsive
prior subjects.
4. The method of claim 1, wherein the gene expression response
signature includes expression levels of a plurality of genes
derived from a cluster of genes associated with response to
anti-TNF therapy on a human interactome map.
5. The method of claim 1, wherein the gene expression response
signature includes expression levels of a plurality of genes
associated with response to anti-TNF therapy and characterized by
their topological properties when mapped on a human interactome
map.
6. The method of claim 1, wherein the anti-TNF therapy is or
comprises administration of infliximab, adalimumab, etanercept,
cirtolizumab pegol, goliluma, or biosimilars thereof.
7. The method of claim 6, wherein the anti-TNF therapy is or
comprises administration of infliximab or adalimumab.
8. The method of claim 1, wherein the disease, disorder, or
condition is selected from rheumatoid arthritis, psoriatic
arthritis, ankylosing spondylitis, Crohn's disease, ulcerative
colitis, chronic psoriasis, hidradenitis suppurativa, and juvenile
idiopathic arthritis.
9. The method of claim 1, further comprising: determining, prior to
the administering, that a subject displays the gene expression
response signature; and administering the anti-TNF therapy to the
subject determined to display the gene expression response
signature.
10. The method of claim 1, further comprising: determining, prior
to the administering, that a subject does not display the gene
expression response signature; and administering a therapy
alternative to anti-TNF therapy to the subject determined not to
display the gene expression response signature.
11. The method of claim 10, wherein the therapy alternative to
anti-TNF therapy is selected from rituximab, sarilumab, tofacitinib
citrate, lefunomide, vedolizumab, tocilizumab, anakinra, and
abatacept.
12. The method of claim 9, wherein the step of determining
comprises measuring gene expression by at least one of a
microarray, RNA sequencing, real-time quantitative reverse
transcription PCR (qRT-PCR), bead array, and ELISA.
13. The method of claim 1, wherein the gene expression response
signature was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to cluster with one
another in a human interactome map, thereby establishing the gene
expression response signature.
14. The method of claim 1, wherein the gene expression response
signature was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to demonstrate
similar topological patterns in a human interactome map, thereby
establishing the gene expression response signature.
15. The method of claim 13, further comprising steps of: validating
the gene expression response signature by assigning probability of
response to a group of known responders and non-responders; and
checking the gene expression response signature against a blinded
group of responders and non-responders.
16. A kit comprising a gene expression response signature
established to distinguish between responsive and non-responsive
prior subjects who have received anti-TNF therapy.
17. A method of determining a gene expression response signature,
the method comprising steps of: mapping genes whose expression
levels significantly correlate to clinical responsiveness or
non-responsiveness of anti-TNF therapy to a human interactome map;
and selecting a plurality of genes determined to cluster exhibit
certain network topological properties with one another in a human
interactome map, thereby establishing the gene expression response
signature.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S.
application Ser. No. 16/394,046, filed Apr. 25, 2019, which is a
continuation of International Application No. PCT/US2019/022588,
filed Mar. 15, 2019, which claims the benefit of U.S. Provisional
Application No. 62/644,070, filed Mar. 16, 2018, each of which is
incorporated by reference herein in its entirety.
BACKGROUND
[0002] Tumor necrosis factor (TNF) is a cell signaling protein
related to regulation of immune cells and apoptosis, and is
implicated in a variety of immune and autoimmune-mediated
disorders. In particular, TNF is known to promote inflammatory
response, which causes many problems associated with autoimmune
disorders, such as rheumatoid arthritis, psoriatic arthritis,
ankylosing spondylitis, Crohn's disease, ulcerative colitis,
inflammatory bowel disease, chronic psoriasis, hidradenitis
suppurativa, asthma, and juvenile idiopathic arthritis.
[0003] TNF-mediated disorders are currently treated by inhibition
of TNF, and in particular by administration of an anti-TNF agent
(i.e., by anti-TNF therapy). Examples of anti-TNF agents approved
in the United States include monoclonal antibodies that target TNF,
such as adalimumab (Humira.RTM.), certolizumab pegol (Cimiza.RTM.),
golimumab (Simponi.RTM. and Simponi Aria.RTM.), and infliximab
(Remicade.RTM.), decoy circulating receptor fusion proteins such as
etanercept (Enbrel.RTM.), and biosimilars, such as adalimumab ABP
501 (AMGEVITA.TM.), and etanercept biosimilars GP2015 (Erelzi).
SUMMARY
[0004] A significant known problem with anti-TNF therapies is that
response rates are inconsistent. Indeed, recent international
conferences designed to bring together leading scientists and
clinicians in the fields of immunology and rheumatology to identify
unmet needs in these fields almost universally identify uncertainty
in response rates as an ongoing challenge. For example, the
19.sup.th annual International Targeted Therapies meeting, which
held break-out sessions relating to challenges in treatment of a
variety of diseases, including rheumatoid arthritis, psoriatic
arthritis, axial spondyloarthritis, systemic lupus erythematous,
and connective tissue diseases (e.g. Sjogren's syndrome, Systemic
sclerosis, vasculitis including Bechet's and IgG4 related disease),
identified certain issues common to all of these diseases,
specifically, "the need for better understanding the heterogeneity
within each disease . . . so that predictive tools for therapeutic
responses can be developed. See Winthrop, et al., "The unmet need
in rheumatology: Reports from the targeted therapies meeting 2017,"
Clin. Immunol. pii: S1521-6616(17)30543-0, Aug. 12, 2017.
Similarly, extensive literature relating to treatment of Crohn's
Disease with anti-TNF therapy consistently bemoans erratic response
rates and inability to predict which patients will benefit. See,
e.g., M. T. Abreu, "Anti-TNF Failures in Crohn's Disease,"
Gastroenterol Hepatol (N.Y.), 7(1):37-39 (January 2011); see also
Ding et al., "Systematic review: predicting and optimising response
to anti-TNF therapy in Crohn's disease--algorithm for practical
management," Aliment Pharmacol. Ther., 43(1):30-51 (January 2016)
(reporting that "[p]rimary nonresponse to anti-TNF treatment
affects 13-40% of patients.").
[0005] Thus, a significant number of patients to whom anti-TNF
therapy is currently being administered do not benefit from the
treatment, and could even be harmed. Known risks of serious
infection and malignancy associated with anti-TNF therapy are so
significant that product approvals typically require so-called
"black box warnings" be included on the label. Other potential side
effects of such therapy include, for example, congestive heart
failure, demyelinating disease, and other systemic side effects.
Furthermore, given that several weeks to months of treatment are
required before a patient is identified as not responding to
anti-TNF therapy (i.e., is a non-responder to anti-TNF therapy),
proper treatment of such patients can be significantly delayed as a
result of the current inability to identify responder vs
non-responder subjects. See, e.g., Roda et al., "Loss of Response
to Anti-TNFs: Definition, Epidemiology, and Management," Clin.
Tranl. Gastroenterol., 7(1):e135 (January 2016) (citing Hanauer et
al., "ACCENT I Study group. Maintenance Infliximab for Crohn's
disease: the ACCENT I randomized trial," Lancet 59:1541-1549
(2002); Sands et al., "Infliximab maintenance therapy for
fistulizing Crohn's disease," N. Engl. J. Med. 350:876-885
(2004)).
[0006] Taken together, particularly given that these anti-TNF
therapies can be quite expensive (typically costing upwards of
$40,000-60,000 per patient per year), these challenges make clear
that technologies capable of defining, identifying, and/or
characterizing responder vs. non-responder patient populations
would represent a significant technological advance, and would
provide significant value to patients and to the healthcare
industry more broadly, including to doctors, regulatory agencies,
and drug developers. The present disclosure provides such
technologies.
[0007] Provided technologies, among other things, permit care
providers to distinguish subjects likely to benefit from anti-TNF
therapy from those who are not, reduce risks to patients, increase
timing and quality of care for non-responder patient populations,
increase efficiency of drug development, and avoid costs associated
with administering ineffective therapy to non-responder patients or
with treating side effects such patients experience upon receiving
anti-TNF therapy.
[0008] Provided technologies embody and/or arise from, among other
things, certain insights that include, for example, identification
of the source of a problem with certain conventional approaches to
defining responder vs. non-responder populations and/or that
represent particularly useful strategies for defining classifiers
that distinguish between such populations. For example, as
described herein, the present disclosure identifies that one source
of a problem with many conventional strategies for defining
responder vs. non-responder populations through consideration of
gene expression differences in the populations is that they
typically prioritize or otherwise focus on highest fold changes;
the present disclosure teaches that such an approach misses subtle
but meaningful differences relevant to disease biology. Moreover,
the present disclosure offers an insight that mapping of genes with
altered expression levels onto a human interactome map (in
particular onto a human interactome map that represents
experimentally supported physical interactions between cellular
components which, in some embodiments, explicitly excludes any
theoretical, calculated, or other interaction that has been
proposed but not experimentally validated), can provide a useful
and effective classifier for defining responders vs. non-responders
to anti-TNF therapy. In some embodiments, genes included in such a
classifier represent a connected module on the human
interactome.
[0009] Accordingly, in some embodiments, the present disclosure
provides a method of treating subjects with anti-TNF therapy, the
method comprising a step of: administering the anti-TNF therapy to
subjects who have been determined to display a gene expression
response signature established to distinguish between responsive
and non-responsive prior subjects who have received the anti-TNF
therapy.
[0010] In some embodiments, the present disclosure provides, in a
method of administering anti-TNF therapy, the improvement that
comprises administering the therapy selectively to subjects who
have been determined to display a gene expression response
signature established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy.
[0011] In some embodiments, the present disclosure provides a kit
comprising a gene expression response signature established to
distinguish between responsive and non-responsive prior subjects
who have received anti-TNF therapy.
[0012] In some embodiments, the present disclosure provides a
method of determining a gene expression response signature, the
method comprising steps of: mapping genes whose expression levels
significantly correlate to clinical responsiveness or
non-responsiveness of anti-TNF therapy to a human interactome map;
and selecting a plurality of genes determined to cluster with one
another in a human interactome map, thereby establishing the gene
expression response signature.
[0013] In some embodiments, the present disclosure provides a
method of determining a gene expression response signature, the
method comprising steps of: mapping genes whose expression levels
significantly correlate to clinical responsiveness or
non-responsiveness of anti-TNF therapy to a human interactome map;
and selecting a plurality of genes associated with response to
anti-TNF therapy and characterized by their topological properties
when mapped on a human interactome map (e.g., genes that are
proximal or otherwise close together in space on a human
interactome map), thereby establishing the gene expression response
signature.
[0014] In some embodiments, the present disclosure provides a
method of determining a gene expression response signature, the
method comprising steps of: mapping genes whose expression levels
significantly correlate to clinical responsiveness or
non-responsiveness of anti-TNF therapy to a human interactome map;
and selecting a plurality of genes selected by diffusion state
distance with one another in a human interactome map, thereby
establishing the gene expression response signature.
Definitions
[0015] Administration: As used herein, the term "administration"
typically refers to the administration of a composition to a
subject or system, for example to achieve delivery of an agent that
is, or is included in or otherwise delivered by, the
composition.
[0016] Agent: As used herein, the term "agent" refers to an entity
(e.g., for example, a lipid, metal, nucleic acid, polypeptide,
polysaccharide, small molecule, etc., or complex, combination,
mixture or system [e.g., cell, tissue, organism] thereof), or
phenomenon (e.g., heat, electric current or field, magnetic force
or field, etc.).
[0017] Amino acid: As used herein, the term "amino acid" refers to
any compound and/or substance that can be incorporated into a
polypeptide chain, e.g., through formation of one or more peptide
bonds. In some embodiments, an amino acid has the general structure
H.sub.2N--C(H)(R)--COOH. In some embodiments, an amino acid is a
naturally-occurring amino acid. In some embodiments, an amino acid
is a non-natural amino acid; in some embodiments, an amino acid is
a D-amino acid; in some embodiments, an amino acid is an L-amino
acid. As used herein, the term "standard amino acid" refers to any
of the twenty L-amino acids commonly found in naturally occurring
peptides. "Nonstandard amino acid" refers to any amino acid, other
than the standard amino acids, regardless of whether it is or can
be found in a natural source. In some embodiments, an amino acid,
including a carboxy- and/or amino-terminal amino acid in a
polypeptide, can contain a structural modification as compared to
the general structure above. For example, in some embodiments, an
amino acid may be modified by methylation, amidation, acetylation,
pegylation, glycosylation, phosphorylation, and/or substitution
(e.g., of the amino group, the carboxylic acid group, one or more
protons, and/or the hydroxyl group) as compared to the general
structure. In some embodiments, such modification may, for example,
alter the stability or the circulating half-life of a polypeptide
containing the modified amino acid as compared to one containing an
otherwise identical unmodified amino acid. In some embodiments,
such modification does not significantly alter a relevant activity
of a polypeptide containing the modified amino acid, as compared to
one containing an otherwise identical unmodified amino acid. As
will be clear from context, in some embodiments, the term "amino
acid" may be used to refer to a free amino acid; in some
embodiments it may be used to refer to an amino acid residue of a
polypeptide, e.g., an amino acid residue within a polypeptide.
[0018] Analog: As used herein, the term "analog" refers to a
substance that shares one or more particular structural features,
elements, components, or moieties with a reference substance.
Typically, an "analog" shows significant structural similarity with
the reference substance, for example sharing a core or consensus
structure, but also differs in certain discrete ways. In some
embodiments, an analog is a substance that can be generated from
the reference substance, e.g., by chemical manipulation of the
reference substance. In some embodiments, an analog is a substance
that can be generated through performance of a synthetic process
substantially similar to (e.g., sharing a plurality of steps with)
one that generates the reference substance. In some embodiments, an
analog is or can be generated through performance of a synthetic
process different from that used to generate the reference
substance.
[0019] Antagonist: As used herein, the term "antagonist" may refer
to an agent, or condition whose presence, level, degree, type, or
form is associated with a decreased level or activity of a target.
An antagonist may include an agent of any chemical class including,
for example, small molecules, polypeptides, nucleic acids,
carbohydrates, lipids, metals, and/or any other entity that shows
the relevant inhibitory activity. In some embodiments, an
antagonist may be a "direct antagonist" in that it binds directly
to its target; in some embodiments, an antagonist may be an
"indirect antagonist" in that it exerts its influence by means
other than binding directly to its target; e.g., by interacting
with a regulator of the target, so that the level or activity of
the target is altered). In some embodiments, an "antagonist" may be
referred to as an "inhibitor".
[0020] Antibody: As used herein, the term "antibody" refers to a
polypeptide that includes canonical immunoglobulin sequence
elements sufficient to confer specific binding to a particular
target antigen. As is known in the art, intact antibodies as
produced in nature are approximately 150 kD tetrameric agents
comprised of two identical heavy chain polypeptides (about 50 kD
each) and two identical light chain polypeptides (about 25 kD each)
that associate with each other into what is commonly referred to as
a "Y-shaped" structure. Each heavy chain is comprised of at least
four domains (each about 110 amino acids long)--an amino-terminal
variable (VH) domain (located at the tips of the Y structure),
followed by three constant domains: CH1, CH2, and the
carboxy-terminal CH3 (located at the base of the Y's stem). A short
region, known as the "switch", connects the heavy chain variable
and constant regions. The "hinge" connects CH2 and CH3 domains to
the rest of the antibody. Two disulfide bonds in this hinge region
connect the two heavy chain polypeptides to one another in an
intact antibody. Each light chain is comprised of two domains--an
amino-terminal variable (VL) domain, followed by a carboxy-terminal
constant (CL) domain, separated from one another by another
"switch". Intact antibody tetramers are comprised of two heavy
chain-light chain dimers in which the heavy and light chains are
linked to one another by a single disulfide bond; two other
disulfide bonds connect the heavy chain hinge regions to one
another, so that the dimers are connected to one another and the
tetramer is formed. Naturally-produced antibodies are also
glycosylated, typically on the CH2 domain. Each domain in a natural
antibody has a structure characterized by an "immunoglobulin fold"
formed from two beta sheets (e.g., 3-, 4-, or 5-stranded sheets)
packed against each other in a compressed antiparallel beta barrel.
Each variable domain contains three hypervariable loops known as
"complement determining regions" (CDR1, CDR2, and CDR3) and four
somewhat invariant "framework" regions (FR1, FR2, FR3, and FR4).
When natural antibodies fold, the FR regions form the beta sheets
that provide the structural framework for the domains, and the CDR
loop regions from both the heavy and light chains are brought
together in three-dimensional space so that they create a single
hypervariable antigen binding site located at the tip of the Y
structure. The Fc region of naturally-occurring antibodies binds to
elements of the complement system, and also to receptors on
effector cells, including for example effector cells that mediate
cytotoxicity. As is known in the art, affinity and/or other binding
attributes of Fc regions for Fc receptors can be modulated through
glycosylation or other modification. In some embodiments,
antibodies produced and/or utilized in accordance with the present
invention include glycosylated Fc domains, including Fc domains
with modified or engineered such glycosylation. For purposes of the
present invention, in certain embodiments, any polypeptide or
complex of polypeptides that includes sufficient immunoglobulin
domain sequences as found in natural antibodies can be referred to
and/or used as an "antibody", whether such polypeptide is naturally
produced (e.g., generated by an organism reacting to an antigen),
or produced by recombinant engineering, chemical synthesis, or
other artificial system or methodology. In some embodiments, an
antibody is polyclonal; in some embodiments, an antibody is
monoclonal. In some embodiments, an antibody has constant region
sequences that are characteristic of mouse, rabbit, primate, or
human antibodies. In some embodiments, antibody sequence elements
are humanized, primatized, chimeric, etc, as is known in the art.
Moreover, the term "antibody" as used herein, can refer in
appropriate embodiments (unless otherwise stated or clear from
context) to any of the art-known or developed constructs or formats
for utilizing antibody structural and functional features in
alternative presentation. For example, embodiments, an antibody
utilized in accordance with the present invention is in a format
selected from, but not limited to, intact IgA, IgG, IgE or IgM
antibodies; bi- or multi-specific antibodies (e.g., Zybodies.RTM.,
etc); antibody fragments such as Fab fragments, Fab' fragments,
F(ab')2 fragments, Fd' fragments, Fd fragments, and isolated CDRs
or sets thereof; single chain Fvs; polypeptide-Fc fusions; single
domain antibodies (e.g., shark single domain antibodies such as
IgNAR or fragments thereof); cameloid antibodies; masked antibodies
(e.g., Probodies.RTM.); Small Modular ImmunoPharmaceuticals
("SMIPs.TM."); single chain or Tandem diabodies (TandAb.RTM.);
VHHs; Anticalins.RTM.; Nanobodies.RTM. minibodies; BiTE.RTM.s;
ankyrin repeat proteins or DARPINs.RTM.; Avimers.RTM.; DARTs;
TCR-like antibodies; Adnectins.RTM.; Affilins.RTM.;
Trans-Bodies.RTM.; Affibodies.RTM.; TrimerX.RTM.; MicroProteins;
Fynomers.RTM., Centyrins.RTM.; and KALBITOR.RTM.s. In some
embodiments, an antibody may lack a covalent modification (e.g.,
attachment of a glycan) that it would have if produced naturally.
In some embodiments, an antibody may contain a covalent
modification (e.g., attachment of a glycan, a payload [e.g., a
detectable moiety, a therapeutic moiety, a catalytic moiety, etc],
or other pendant group [e.g., poly-ethylene glycol, etc.]).
[0021] Associated: Two events or entities are "associated" with one
another, as that term is used herein, if the presence, level,
degree, type and/or form of one is correlated with that of the
other. For example, a particular entity (e.g., polypeptide, genetic
signature, metabolite, microbe, etc) is considered to be associated
with a particular disease, disorder, or condition, if its presence,
level and/or form correlates with incidence of and/or
susceptibility to the disease, disorder, or condition (e.g., across
a relevant population). In some embodiments, two or more entities
are physically "associated" with one another if they interact,
directly or indirectly, so that they are and/or remain in physical
proximity with one another. In some embodiments, two or more
entities that are physically associated with one another are
covalently linked to one another; in some embodiments, two or more
entities that are physically associated with one another are not
covalently linked to one another but are non-covalently associated,
for example by means of hydrogen bonds, van der Waals interaction,
hydrophobic interactions, magnetism, and combinations thereof.
[0022] Biological Sample: As used herein, the term "biological
sample" typically refers to a sample obtained or derived from a
biological source (e.g., a tissue or organism or cell culture) of
interest, as described herein. In some embodiments, a source of
interest comprises an organism, such as an animal or human. In some
embodiments, a biological sample is or comprises biological tissue
or fluid. In some embodiments, a biological sample may be or
comprise bone marrow; blood; blood cells; ascites; tissue or fine
needle biopsy samples; cell-containing body fluids; free floating
nucleic acids; sputum; saliva; urine; cerebrospinal fluid,
peritoneal fluid; pleural fluid; feces; lymph; gynecological
fluids; skin swabs; vaginal swabs; oral swabs; nasal swabs;
washings or lavages such as a ductal lavages or broncheoalveolar
lavages; aspirates; scrapings; bone marrow specimens; tissue biopsy
specimens; surgical specimens; feces, other body fluids,
secretions, and/or excretions; and/or cells therefrom, etc. In some
embodiments, a biological sample is or comprises cells obtained
from an individual. In some embodiments, obtained cells are or
include cells from an individual from whom the sample is obtained.
In some embodiments, a sample is a "primary sample" obtained
directly from a source of interest by any appropriate means. For
example, in some embodiments, a primary biological sample is
obtained by methods selected from the group consisting of biopsy
(e.g., fine needle aspiration or tissue biopsy), surgery,
collection of body fluid (e.g., blood, lymph, feces etc.), etc. In
some embodiments, as will be clear from context, the term "sample"
refers to a preparation that is obtained by processing (e.g., by
removing one or more components of and/or by adding one or more
agents to) a primary sample. For example, filtering using a
semi-permeable membrane. Such a "processed sample" may comprise,
for example nucleic acids or proteins extracted from a sample or
obtained by subjecting a primary sample to techniques such as
amplification or reverse transcription of mRNA, isolation and/or
purification of certain components, etc.
[0023] Combination Therapy: As used herein, the term "combination
therapy" refers to a clinical intervention in which a subject is
simultaneously exposed to two or more therapeutic regimens (e.g.
two or more therapeutic agents). In some embodiments, the two or
more therapeutic regimens may be administered simultaneously. In
some embodiments, the two or more therapeutic regimens may be
administered sequentially (e.g., a first regimen administered prior
to administration of any doses of a second regimen). In some
embodiments, the two or more therapeutic regimens are administered
in overlapping dosing regimens. In some embodiments, administration
of combination therapy may involve administration of one or more
therapeutic agents or modalities to a subject receiving the other
agent(s) or modality. In some embodiments, combination therapy does
not necessarily require that individual agents be administered
together in a single composition (or even necessarily at the same
time). In some embodiments, two or more therapeutic agents or
modalities of a combination therapy are administered to a subject
separately, e.g., in separate compositions, via separate
administration routes (e.g., one agent orally and another agent
intravenously), and/or at different time points. In some
embodiments, two or more therapeutic agents may be administered
together in a combination composition, or even in a combination
compound (e.g., as part of a single chemical complex or covalent
entity), via the same administration route, and/or at the same
time.
[0024] Comparable: As used herein, the term "comparable" refers to
two or more agents, entities, situations, sets of conditions, etc.,
that may not be identical to one another but that are sufficiently
similar to permit comparison there between so that one skilled in
the art will appreciate that conclusions may reasonably be drawn
based on differences or similarities observed. In some embodiments,
comparable sets of conditions, circumstances, individuals, or
populations are characterized by a plurality of substantially
identical features and one or a small number of varied features.
Those of ordinary skill in the art will understand, in context,
what degree of identity is required in any given circumstance for
two or more such agents, entities, situations, sets of conditions,
etc. to be considered comparable. For example, those of ordinary
skill in the art will appreciate that sets of circumstances,
individuals, or populations are comparable to one another when
characterized by a sufficient number and type of substantially
identical features to warrant a reasonable conclusion that
differences in results obtained or phenomena observed under or with
different sets of circumstances, individuals, or populations are
caused by or indicative of the variation in those features that are
varied.
[0025] Corresponding to: As used herein, the phrase "corresponding
to" refers to a relationship between two entities, events, or
phenomena that share sufficient features to be reasonably
comparable such that "corresponding" attributes are apparent. For
example, in some embodiments, the term may be used in reference to
a compound or composition, to designate the position and/or
identity of a structural element in the compound or composition
through comparison with an appropriate reference compound or
composition. For example, in some embodiments, a monomeric residue
in a polymer (e.g., an amino acid residue in a polypeptide or a
nucleic acid residue in a polynucleotide) may be identified as
"corresponding to" a residue in an appropriate reference polymer.
For example, those of ordinary skill will appreciate that, for
purposes of simplicity, residues in a polypeptide are often
designated using a canonical numbering system based on a reference
related polypeptide, so that an amino acid "corresponding to" a
residue at position 190, for example, need not actually be the
190.sup.th amino acid in a particular amino acid chain but rather
corresponds to the residue found at 190 in the reference
polypeptide; those of ordinary skill in the art readily appreciate
how to identify "corresponding" amino acids. For example, those
skilled in the art will be aware of various sequence alignment
strategies, including software programs such as, for example,
BLAST.RTM., CS-BLAST.RTM., CUSASW++, DIAMOND, FASTA.TM.,
GGSEARCH/GLSEARCH, Genoogle, HMMER.TM., HHpred/HHsearch, IDF,
Infernal, KLAST, USEARCH, parasail, PSI-BLAST.RTM., PSI-Search,
ScalaBLAST.RTM., Sequilab, SAM, SSEARCH, SWAPHI, SWAPHI-LS, SWIMM,
or SWIPE that can be utilized, for example, to identify
"corresponding" residues in polypeptides and/or nucleic acids in
accordance with the present disclosure.
[0026] Dosing regimen: As used herein, the term "dosing regimen"
refers to a set of unit doses (typically more than one) that are
administered individually to a subject, typically separated by
periods of time. In some embodiments, a given therapeutic agent has
a recommended dosing regimen, which may involve one or more doses.
In some embodiments, a dosing regimen comprises a plurality of
doses each of which is separated in time from other doses. In some
embodiments, individual doses are separated from one another by a
time period of the same length; in some embodiments, a dosing
regimen comprises a plurality of doses and at least two different
time periods separating individual doses. In some embodiments, all
doses within a dosing regimen are of the same unit dose amount. In
some embodiments, different doses within a dosing regimen are of
different amounts. In some embodiments, a dosing regimen comprises
a first dose in a first dose amount, followed by one or more
additional doses in a second dose amount different from the first
dose amount. In some embodiments, a dosing regimen comprises a
first dose in a first dose amount, followed by one or more
additional doses in a second dose amount same as the first dose
amount. In some embodiments, a dosing regimen is correlated with a
desired or beneficial outcome when administered across a relevant
population (i.e., is a therapeutic dosing regimen).
[0027] Improved, increased or reduced: As used herein, the terms
"improved," "increased," or "reduced,", or grammatically comparable
comparative terms thereof, indicate values that are relative to a
comparable reference measurement. For example, in some embodiments,
an assessed value achieved with an agent of interest may be
"improved" relative to that obtained with a comparable reference
agent. Alternatively or additionally, in some embodiments, an
assessed value achieved in a subject or system of interest may be
"improved" relative to that obtained in the same subject or system
under different conditions (e.g., prior to or after an event such
as administration of an agent of interest), or in a different,
comparable subject (e.g., in a comparable subject or system that
differs from the subject or system of interest in presence of one
or more indicators of a particular disease, disorder or condition
of interest, or in prior exposure to a condition or agent,
etc.).
[0028] Pharmaceutical composition: As used herein, the term
"pharmaceutical composition" refers to an active agent, formulated
together with one or more pharmaceutically acceptable carriers. In
some embodiments, the active agent is present in unit dose amounts
appropriate for administration in a therapeutic regimen to a
relevant subject (e.g., in amounts that have been demonstrated to
show a statistically significant probability of achieving a
predetermined therapeutic effect when administered), or in a
different, comparable subject (e.g., in a comparable subject or
system that differs from the subject or system of interest in
presence of one or more indicators of a particular disease,
disorder or condition of interest, or in prior exposure to a
condition or agent, etc.). In some embodiments, comparative terms
refer to statistically relevant differences (e.g., that are of a
prevalence and/or magnitude sufficient to achieve statistical
relevance). Those skilled in the art will be aware, or will readily
be able to determine, in a given context, a degree and/or
prevalence of difference that is required or sufficient to achieve
such statistical significance.
[0029] Pharmaceutically acceptable: As used herein, the phrase
"pharmaceutically acceptable" refers to those compounds, materials,
compositions, and/or dosage forms which are, within the scope of
sound medical judgment, suitable for use in contact with the
tissues of human beings and animals without excessive toxicity,
irritation, allergic response, or other problem or complication,
commensurate with a reasonable benefit/risk ratio.
[0030] Primary Non-Responder: As used herein, the term "primary
non-responder" refers to a subject that displays a lack of
improvement in clinical signs and symptoms after receiving anti-TNF
therapy for a period of time. Those skilled in the art will
understand that the medical community may establish an appropriate
period of time for any particular disease or condition, or for any
particular patient or patient type. To give but a few examples, in
some embodiments, the period of time may be at least 8 weeks. In
some embodiments, the period of time may be at least 12 weeks. In
some embodiments, the period of time may be 14 weeks.
[0031] Reference: As used herein, the term "reference" describes a
standard or control relative to which a comparison is performed.
For example, in some embodiments, an agent, animal, individual,
population, sample, sequence or value of interest is compared with
a reference or control agent, animal, individual, population,
sample, sequence or value. In some embodiments, a reference or
control is tested and/or determined substantially simultaneously
with the testing or determination of interest. In some embodiments,
a reference or control is a historical reference or control,
optionally embodied in a tangible medium. Typically, as would be
understood by those skilled in the art, a reference or control is
determined or characterized under comparable conditions or
circumstances to those under assessment. Those skilled in the art
will appreciate when sufficient similarities are present to justify
reliance on and/or comparison to a particular possible reference or
control.
[0032] Secondary Non-Responder: As used herein, the term "secondary
non-responder" refers to a subject that displays an initial
improvement in clinical signs or symptoms after receiving anti-TNF
therapy, but show a statistically significant decrease in such
improvement over time.
[0033] Therapeutically effective amount: As used herein, the term
"therapeutically effective amount" refers to an amount of a
substance (e.g., a therapeutic agent, composition, and/or
formulation) that elicits a desired biological response when
administered as part of a therapeutic regimen. In some embodiments,
a therapeutically effective amount of a substance is an amount that
is sufficient, when administered to a subject suffering from or
susceptible to a disease, disorder, and/or condition, to treat,
diagnose, prevent, and/or delay the onset of the disease, disorder,
and/or condition. As will be appreciated by those of ordinary skill
in this art, the effective amount of a substance may vary depending
on such factors as the desired biological endpoint, the substance
to be delivered, the target cell or tissue, etc. For example, the
effective amount of compound in a formulation to treat a disease,
disorder, and/or condition is the amount that alleviates,
ameliorates, relieves, inhibits, prevents, delays onset of, reduces
severity of and/or reduces incidence of one or more symptoms or
features of the disease, disorder and/or condition. In some
embodiments, a therapeutically effective amount is administered in
a single dose; in some embodiments, multiple unit doses are
required to deliver a therapeutically effective amount.
[0034] Variant: As used herein, the term "variant" refers to an
entity that shows significant structural identity with a reference
entity but differs structurally from the reference entity in the
presence or level of one or more chemical moieties as compared with
the reference entity. In many embodiments, a variant also differs
functionally from its reference entity. In general, whether a
particular entity is properly considered to be a "variant" of a
reference entity is based on its degree of structural identity with
the reference entity. As will be appreciated by those skilled in
the art, any biological or chemical reference entity has certain
characteristic structural elements. A variant, by definition, is a
distinct chemical entity that shares one or more such
characteristic structural elements. To give but a few examples, a
small molecule may have a characteristic core structural element
(e.g., a macrocycle core) and/or one or more characteristic pendent
moieties so that a variant of the small molecule is one that shares
the core structural element and the characteristic pendent moieties
but differs in other pendent moieties and/or in types of bonds
present (single vs double, E vs Z, etc.) within the core, a
polypeptide may have a characteristic sequence element comprised of
a plurality of amino acids having designated positions relative to
one another in linear or three-dimensional space and/or
contributing to a particular biological function, a nucleic acid
may have a characteristic sequence element comprised of a plurality
of nucleotide residues having designated positions relative to on
another in linear or three-dimensional space. For example, a
variant polypeptide may differ from a reference polypeptide as a
result of one or more differences in amino acid sequence and/or one
or more differences in chemical moieties (e.g., carbohydrates,
lipids, etc.) covalently attached to the polypeptide backbone. In
some embodiments, a variant polypeptide shows an overall sequence
identity with a reference polypeptide that is at least 85%, 86%,
87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, or 99%.
Alternatively or additionally, in some embodiments, a variant
polypeptide does not share at least one characteristic sequence
element with a reference polypeptide. In some embodiments, the
reference polypeptide has one or more biological activities. In
some embodiments, a variant polypeptide shares one or more of the
biological activities of the reference polypeptide. In some
embodiments, a variant polypeptide lacks one or more of the
biological activities of the reference polypeptide. In some
embodiments, a variant polypeptide shows a reduced level of one or
more biological activities as compared with the reference
polypeptide. In many embodiments, a polypeptide of interest is
considered to be a "variant" of a parent or reference polypeptide
if the polypeptide of interest has an amino acid sequence that is
identical to that of the parent but for a small number of sequence
alterations at particular positions. Typically, fewer than 20%,
15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% of the residues in the
variant are substituted as compared with the parent. In some
embodiments, a variant has 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1
substituted residue as compared with a parent. Often, a variant has
a very small number (e.g., fewer than 5, 4, 3, 2, or 1) number of
substituted functional residues (i.e., residues that participate in
a particular biological activity). Furthermore, a variant typically
has not more than 5, 4, 3, 2, or 1 additions or deletions, and
often has no additions or deletions, as compared with the parent.
Moreover, any additions or deletions are typically fewer than about
25, about 20, about 19, about 18, about 17, about 16, about 15,
about 14, about 13, about 10, about 9, about 8, about 7, about 6,
and commonly are fewer than about 5, about 4, about 3, or about 2
residues. In some embodiments, the parent or reference polypeptide
is one found in nature.
BRIEF DESCRIPTION OF THE DRAWING
[0035] FIGS. 1A and 1B are plots illustrating ulcerative colitis
(UC) response signature genes modules detected using the human
interactome (HI) from the UC cohort. The response signature genes
found in gene expression data form a significant cluster when
mapped to the HI (FIG. 1A) and is much larger than expected by
chance (FIG. 1B) which reflects an underlying biology of
response.
[0036] FIGS. 2A and 2B are plots illustrating in-cohort performance
of response predictions of a near perfect classifier using
leave-one-out cross-validation. FIG. 2A is a receiver operating
characteristic (ROC) curve and FIG. 2B illustrates the Negative
Predictive Value (NPV) vs. True Negative Rate (TNR) curve. The
classifier is able to detect 70% of the non-responders with 100%
accuracy, and 100% of the non-responders with 90% accuracy.
[0037] FIGS. 3A and 3B are plots illustrating cross-cohort
performance of response prediction classifier when testing on an
independent cohort. FIG. 3A is an ROC curve and FIG. 3B illustrates
the NPV vs. TNR curve. The classifier is able to detect 50% of the
non-responders with 100% accuracy.
[0038] FIGS. 4A, 4B, 4C, and 4D are plots illustrating in-cohort
rheumatoid arthritis (RA) classifier validation using leave-one-out
cross validation when training on Feature Set 1 (FIGS. 4A and 4B)
and top nine signature genes (FIGS. 4C and 4D).
[0039] FIGS. 5A and 5B are plots illustrating ROC curves of cross
cohort classifier test results (in FIG. 5A) and negative predictive
performance (in FIG. 5B) for the RA classifier.
[0040] FIG. 6 is an exemplary workflow for developing a
classifier.
DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS
[0041] As noted, the response rate for patients undergoing anti-TNF
therapy is inconsistent. Technologies that reliably identify
responsive or non-responsive subjects would be beneficial, as they
would avoid wasteful and even potentially damaging administration
of therapy to subjects who will not respond, and furthermore would
allow timely determination of more appropriate treatment for such
subjects. The present disclosure provides such technologies,
addressing needs of patients, their families, drug developers, and
medical professionals each of whom suffers under the current
system.
[0042] While significant effort has been invested in efforts to
develop technologies that reliably predict responsiveness (e.g., by
identifying responsive vs. non-responsive populations) or
development of resistance for certain therapeutic agents, regimens,
or modalities, success has been elusive, and almost exclusively
limited to the oncology sector. Complex disorders, such as
autoimmune and/or cardiovascular diseases have proven to be
particularly challenging.
[0043] Cancer is typically associated with particular strong driver
genes, which dramatically simplifies the analysis required to
identify responder vs non-responder patient populations, and
significantly improves success rates. By contrast, diseases
associated with more complex genetic (and/or epigenetic)
contributions, have thus far presented an insurmountable challenge
for available technologies.
[0044] Indeed, a large number of published reports describe efforts
to develop technologies for predicting responsiveness to anti-TNF
therapy in inflammatory conditions (e.g., rheumatoid arthritis),
most commonly relying on blood-based gene expression classifiers.
See, e.g., Nakamura et al. "Identification of baseline gene
expression signatures predicting therapeutic responses to three
biologic agents in rheumatoid arthritis: a retrospective
observational study" Arthritis Research & Therapy (2016) 18:159
DOI 10.1186/s13075-016-1052-8. However, a clinically utilizable
classifier has not yet been identified. Notably, Toonen et al.
performed an independent study that tested eight different gene
expression signatures predicting response to anti-TNF, and reported
that most signatures failed to demonstrate sufficient predictive
value to be of utility. See M. Toonen et al., "Validation Study of
Existing Gene Expression Signatures for Anti-TNF Treatment in
Patients with Rheumatoid Arthritis," PLOS ONE 7(3): e33199. Thomson
et al. attempted to describe a blood-based classifier to identify
non-responders to one anti-TNF therapy, infliximab, in rheumatoid
arthritis. Thomson et al., "Blood-based identification of
non-responders to anti-TNF therapy in rheumatoid arthritis," BMC
Med Genomics, 8:26, *142 (2015). Their proposed classifier
comprised 18 signaling mechanisms indicative of higher TNF-mediated
inflammatory signaling in responders at baseline, versus higher
levels of specific metabolic activities in non-responders at
baseline. The test, however, did not reach the level of predictive
accuracy required for commercialization and so development was
stopped.
[0045] Typically, conventional strategies for defining responder
vs. non-responder classifiers for anti-TNF therapy rely on
machine-learning approaches, using mean values across classes of
response, and focusing on genes with the highest fold changes,
often in a pathway-based context. The present disclosure identifies
various sources of problems with these conventional approaches,
and, moreover, provides technologies that solve or avoid the
problems, thereby satisfying the long felt need within the
community for accurate and/or useful predictive classifiers.
[0046] Among other things, the present disclosure appreciates that
machine learning may be useful for finding correlation between
datasets of patients, but fails to achieve sufficient predictive
accuracy across cohorts. Furthermore, the present disclosure
identifies that prioritizing or otherwise focusing on highest fold
changes misses subtle but meaningful differences relevant to
disease biology. Still further, the present disclosure offers an
insight that mapping of genes with altered expression levels onto a
human interactome (e.g., that represents experimentally supported
physical interactions between cellular components and, in some
embodiments, explicitly excludes any theoretical, calculated, or
other interaction that has been proposed but not experimentally
validated) can provide a useful and effective classifier for
defining responders vs. non-responders to anti-TNF therapy. In some
embodiments, genes included in such a classifier represent a
connected module in the human interactome.
Anti-TNF Therapy
[0047] TNF-mediated disorders are currently treated by inhibition
of TNF, and in particular by administration of an anti-TNF agent
(i.e., by anti-TNF therapy). Examples of anti-TNF agents approved
for use in the United States include monoclonal antibodies such as
adalimumab (Humira.RTM.), certolizumab pegol (Cimiza.RTM.),
infliximab (Remicade.RTM.), and decoy circulating receptor fusion
proteins such as etanercept (Enbrel.RTM.). These agents are
currently approved for use in treatment of indications, according
to dosing regimens, as set forth below in Table 1:
TABLE-US-00001 TABLE 1 Indication Adalimumab.sup.1 Certolizumab
Pegol.sup.1 Infliximab.sup.2 Etanercept.sup.1 Golimumab.sup.1
Golimumab.sup.2 Juvenile 10 kg (22 lbs) N/A N/A 0.8 mg/kg weekly,
N/A N/A Idiopathic to <15 kg (33 lbs): with a maximum of
Arthritis 10 mg every 50 mg per week other week 15 kg (33 lbs) to
<30 kg (66 lbs): 20 mg every other week .gtoreq.30 kg (66 lbs):
40 mg every other week Psoriatic 40 mg every other 400 mg initially
and 5 mg/kg at 0, 2 and 6 50 mg once weekly 50 mg administered N/A
Arthritis week at week 2 and 4, weeks, then every 8 with or without
by subcutaneous followed by 200 mg weeks methotrexate injection
once a every other week; for month maintenance dosing, 400 mg every
4 weeks Rheumatoid 40 mg every other 400 mg initially and In
conjunction with 50 mg once weekly 50 mg once a month 2 mg/kg
intravenous Arthritis week at Weeks 2 and 4, methotrexate, 3 with
or without infusion over 30 followed by 200 mg mg/kg at 0, 2 and 6
methotrexate minutes at weeks 0 every other week; for weeks, then
every 8 and 4, then every 8 maintenance dosing, weeks weeks 400 mg
every 4 weeks Ankylosing 40 mg every other 400 mg (given as 2 5
mg/kg at 0, 2 and 6 50 mg once weekly 50 mg administered N/A
Spondylitis week subcutaneous weeks, then every 6 by subcutaneous
injections of 200 mg weeks injection once a each) initially and at
month weeks 2 and 4, followed by 200 mg every other week or 400 mg
every 4 weeks Adult Initial dose (Day 400 mg initially 5 mg/kg at
0, 2 and 6 N/A N/A N/A Crohn's 1): 160 mg and at Weeks 2 weeks,
then every 8 Disease Second dose two and 4 weeks. weeks later (Day
Continue with 15): 80 mg 400 mg every Two weeks four weeks later
(Day 29): Begin a maintenance dose of 40 mg every other week
Pediatric 17 kg (37 lbs) N/A 5 mg/kg at 0, 2 and 6 N/A N/A N/A
Crohn's to <40 kg (88 lbs): weeks, then every 8 Disease Initial
dose (Day weeks. 1): 80 mg Second dose two weeks later (Day 15): 40
mg Two weeks later (Day 29): Begin a maintenance dose of 20 mg
every other week .gtoreq.40 kg (88 lbs): Initial dose (Day 1): 160
mg Second dose two weeks later (Day 15): 80 mg Two weeks later (Day
29): Begin a maintenance dose of 40 mg every other week Ulcerative
Initial dose (Day N/A 5 mg/kg at 0, 2 and 6 N/A N/A N/A Colitis 1):
160 mg weeks, then every 8 Second dose two weeks. weeks later (Day
15): 80 mg Two weeks later (Day 29): Begin a maintenance dose of 40
mg every other week Plaque 80 mg initial dose; 40 N/A N/A 50 mg
twice weekly N/A N/A Psoriasis mg every other week for 3 months,
beginning one week followed by 50 mg after initial dose once weekly
Hidradenitis Initial dose (Day N/A N/A N/A N/A N/A Suppurativa 1):
160 mg Second dose two weeks later (Day 15): 80 mg Third dose (Day
29) and subsequent doses: 40 mg every week Uveitis 80 mg initial
dose; 40 N/A N/A N/A N/A N/A mg every other week beginning one week
after initial dose .sup.1Administered by subcutaneous injection.
.sup.2Administered by intravenous infusion.
[0048] The present disclosure provides technologies relevant to
anti-TNF therapy, including those therapeutic regimens as set forth
in Table 1. In some embodiments, the anti-TNF therapy is or
comprises administration of infliximab (Remicade.RTM.), adalimumab
(Humira.RTM.), certolizumab pegol (Cimiza.RTM.), etanercept
(Enbel.RTM.), or biosimilars thereof. In some embodiments, the
anti-TNF therapy is or comprises administration of infliximab
(Remicade.RTM.) or adalimumab (Humira.RTM.). In some embodiments,
the anti-TNF therapy is or comprises administration of infliximab
(Remicade.RTM.). In some embodiments, the anti-TNF therapy is or
comprises administration of adalimumab (Humira.RTM.).
[0049] In some embodiments, the anti-TNF therapy is or comprises
administration of a biosimilar anti-TNF agent. In some embodiments,
the anti-TNF agent is selected from infliximab biosimilars such as
CT-P13, BOW015, SB2, Inflectra, Renflexis, and Ixifi, adalimumab
biosimilars such as ABP 501 (AMGEVITA.TM.), Adfrar, and Hulin.TM.
and etanercept biosimilars such as HD203, SB4 (Benepali.RTM.),
GP2015, Erelzi, and Intacept.
[0050] In some embodiments, the present disclosure defines patient
populations to whom anti-TNF therapy should (or should not) be
administered. In some embodiments, technologies provided by the
present disclosure generate information useful to doctors,
pharmaceutical companies, payers, and/or regulatory agencies who
wish to ensure that anti-TNF therapy is administered to responder
populations and/or is not administered to non-responder
populations.
Diseases, Disorders or Conditions
[0051] In general, provided disclosures are useful in any context
in which administration of anti-TNF therapy is contemplated or
implemented. In some embodiments, provided technologies are useful
in the diagnosis and/or treatment of subjects suffering from a
disease, disorder, or condition associated with aberrant (e.g.,
elevated) TNF expression and/or activity. In some embodiments,
provided technologies are useful in monitoring subjects who are
receiving or have received anti-TNF therapy. In some embodiments,
provided technologies identify whether a subject will or will not
respond to a given anti-TNF therapy. In some embodiments, the
provided technologies identify whether a subject will develop
resistance to a given anti-TNF therapy.
[0052] Accordingly, the present disclosure provides technologies
relevant to treatment of the various disorders related to TNF,
including those listed in Table 1. In some embodiments, a subject
is suffering from a disease, disorder, or condition selected from
rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis,
Crohn's disease (adult or pediatric), ulcerative colitis,
inflammatory bowel disease, chronic psoriasis, plaque psoriasis,
hidradenitis suppurativa, asthma, uveitis, and juvenile idiopathic
arthritis. In some embodiments, the disease, disorder, or condition
is rheumatoid arthritis. In some embodiments, the disease,
disorder, or condition is psoriatic arthritis. In some embodiments,
the disease, disorder, or condition is ankylosing spondylitis. In
some embodiments, the disease, disorder, or condition is Crohn's
disease. In some embodiments, the disease, disorder, or condition
is adult Crohn's disease. In some embodiments, the disease,
disorder, or condition is pediatric Crohn's disease. In some
embodiments, the disease, disorder, or condition is inflammatory
bowel disease. In some embodiments, the disease, disorder, or
condition is ulcerative colitis. In some embodiments, the disease,
disorder, or condition is chronic psoriasis. In some embodiments,
the disease, disorder, or condition is plaque psoriasis. In some
embodiments, the disease, disorder, or condition is hidradenitis
suppurativa. In some embodiments, the disease, disorder, or
condition is asthma. In some embodiments, the disease, disorder, or
condition is uveitis. In some embodiments, the disease, disorder,
or condition is juvenile idiopathic arthritis.
Provided Classifier(s)
[0053] The present disclosure provides gene expression response
signatures that serve as gene classifiers and identify (i.e.,
predict) which patients will or will not respond to anti-TNF
therapy. That is, the present disclosure provides methods of
determining gene expression response signatures that are
characteristic of anti-TNF responder or non-responder populations.
In some embodiments, a particular gene expression response
signature classifies responder or non-responder populations for a
particular anti-TNF therapy (e.g., a particular anti-TNF agent
and/or regimen). In some embodiments, responder and/or
non-responder populations for different anti-TNF therapies (e.g.,
different anti-TNF agents and/or regimens) may overlap or be
co-extensive; in some such embodiments, the present disclosure may
provide gene expression response signatures that serve as gene
classifiers for responder and/or non-responder populations across
anti-TNF therapies.
[0054] In some embodiments, as described herein, a gene expression
response signature is identified by retrospective analysis of gene
expression levels in biological samples from patients who have
received anti-TNF therapy and have been determined to respond
(i.e., are responders) or not to respond (i.e., are
non-responders). In some embodiments, all such patients have
received the same anti-TNF therapy (optionally for the same or
different periods of time); alternatively or additionally, in some
embodiments, all such patients have been diagnosed with the same
disease, disorder or condition. In some embodiments, patients whose
biological samples are analyzed in the retrospective analysis had
received different anti-TNF therapy (e.g., with a different
anti-TNF agent and/or according to a different regimen);
alternatively or additionally, in some embodiments, patients whose
biological samples are analyzed in the retrospective analysis have
been diagnosed with different diseases, disorders, or
conditions.
[0055] Typically, a gene expression response signature as described
herein is determined by comparison of gene expression levels in the
responder vs. non-responder populations whose biological samples
are analyzed in a retrospective analysis as described herein. Genes
whose expression levels show statistically significant differences
between the responder and non-responder populations may be included
in the gene response signature.
[0056] In some embodiments, the present disclosure embodies an
insight that the source of a problem with certain prior efforts to
identify or provide gene expression response signatures through
comparison of gene expression levels in responder vs non-responder
populations have emphasized and/or focused on (often solely on)
genes that show the largest difference (e.g., greater than 2-fold
change) in expression levels between the populations. The present
disclosure appreciates that even genes those expression level
differences are relatively small (e.g., less than 2-fold change in
expression) provide useful information and are valuably included in
a gene expression response signature in embodiments described
herein.
[0057] Moreover, in some embodiments, the present disclosure
embodies an insight that analysis of interaction patterns of genes
whose expression levels show statistically significant differences
(optionally including small differences) between responder and
non-responder populations as described herein provides new and
valuable information that materially improves the quality and
predictive power of a gene expression response signature.
[0058] Further, as noted, the present disclosure provides
technologies that allow practitioners to reliably and consistently
predict response in a cohort of subjects. In particular, for
example, the rate of response for some anti-TNF therapies is less
than 35% within a given cohort of subjects. The provided
technologies allow for prediction of greater than 65% accuracy
within a cohort of subjects a response rate (i.e., whether certain
subjects will or will not respond to a given therapy). In some
embodiments, the methods and systems described herein predict 65%
or greater the subjects that are responders (i.e., will respond to
anti-TNF therapy) within a given cohort. In some embodiments, the
methods and systems described herein predict 70% or greater the
subjects that are responders within a given cohort. In some
embodiments, the methods and systems described herein predict 80%
or greater the subjects that are responders within a given cohort.
In some embodiments, the methods and systems described herein
predict 90% or greater the subjects that are responders within a
given cohort. In some embodiments, the methods and systems
described herein predict 100% the subjects that are responders
within a given cohort. In some embodiments, the methods and systems
described herein predict 65% or greater the subjects that are
non-responders (i.e., will not respond to anti-TNF therapy) within
a given cohort. In some embodiments, the methods and systems
described herein predict 70% or greater the subjects that are
non-responders within a given cohort. In some embodiments, the
methods and systems described herein predict 80% or greater the
subjects that are non-responders within a given cohort. In some
embodiments, the methods and systems described herein predict 90%
or greater the subjects that are non-responders within a given
cohort. In some embodiments, the methods and systems described
herein predict 100% of the subjects that are non-responders within
a given cohort.
Defining Classifier(s)
[0059] A provided gene expression response signature (i.e., a gene
classifier) is a gene or set of genes that can be used to determine
whether a subject will or will not respond to a particular therapy
(e.g., anti-TNF therapy). In some embodiments, a gene expression
response signature can be identified using mRNA and/or protein
expression datasets, for example as may be or have been prepared
from validated biological data (e.g., biological data derived from
publicly available databases such as Gene Expression Omnibus
("GEO")). In some embodiments, a gene expression response signature
may be derived by comparing gene expression levels of known
responsive and known non-responsive prior subjects to a specific
therapy (e.g., anti-TNF therapy). In some embodiments, certain
genes (i.e., signature genes) are selected from this cohort of gene
expression data to be used in developing the gene expression
response signature.
[0060] In some embodiments, signature genes are identified by
methods analogous to those reported by Santolini, "A personalized,
multiomics approach identifies genes involved in cardiac
hypertrophy and heart failure," Systems Biology and Applications,
(2018)4:12; doi:10.1038/s41540-018-0046-3, which is incorporated
herein by reference. In some embodiments, signature genes are
identified by comparing gene expression levels of known responsive
and non-responsive prior subjects and identifying significant
changes between the two groups, wherein the significant changes can
be large differences in expression (e.g., greater than 2-fold
change), small differences in expression (e.g., less than 2-fold
change), or both. In some embodiments, genes are ranked by
significance of difference in expression. In some embodiments,
significance is measured by Pearson correlation between gene
expression and response outcome. In some embodiments, signature
genes are selected from the ranking by significance of difference
in expression. In some embodiments, the number of signature genes
selected is less than the total number of genes analyzed. In some
embodiments, 200 signature genes or less are selected. In some
embodiments 100 genes or less are selected.
[0061] In some embodiments, signature genes are selected in
conjunction with their location on a human interactome (HI), a map
of protein-protein interactions. Use of the HI in this way
encompasses a recognition that mRNA activity is dynamic and
determines the actual over and under expression of proteins
critical to understanding certain diseases. In some embodiments,
genes associated with response to certain therapies (i.e., anti-TNF
therapy) may cluster (i.e., form a cluster of genes) in discrete
modules on the HI map. The existence of such clusters is associated
with the existence of fundamental underlying disease biology. In
some embodiments, a gene expression response signature is derived
from signature genes selected from the cluster of genes on the HI
map. Accordingly, in some embodiments, a gene expression response
signature is derived from a cluster of genes associated with
response to anti-TNF therapy on a human interactome map.
[0062] In some embodiments, genes associated with response to
certain therapies exhibit certain topological properties when
mapped onto a human interactome map. For example, in some
embodiments, a plurality of genes associated with response to
anti-TNF therapy and characterized by their position (i.e.,
topological properties, e.g., their proximity to one another) on a
human interactome map.
[0063] In some embodiments, genes associated with response to
certain therapies (i.e., anti-TNF therapy) may exist within close
proximity to one another on the HI map. Said proximal genes, do not
necessarily need to share fundamental underlying disease biology.
That is, in some embodiments, proximal genes do not share
significant protein interaction. Accordingly, in some embodiments,
the gene expression response signature is derived from genes that
are proximal on a human interactome map. In some embodiments, the
gene expression response signature is derived from certain other
topological features on a human interactome map.
[0064] In some embodiments, genes associated with response to
certain therapies (i.e., anti-TNF therapy) may be determined by
Diffusion State Distance (DSD) (see Cao, et al., PLOS One, 8(10):
e76339 (Oct. 23, 2013)) when used in combination with the HI
map.
[0065] In some embodiments, signature genes are selected by (1)
ranking genes based on the significance of difference of expression
of genes as compared to known responders and known non-responders;
(2) selecting genes from the ranked genes and mapping the selected
genes onto a human interactome map; and (3) selecting signature
genes from the genes mapped onto the human interactome map.
[0066] In some embodiments, signature genes (e.g., selected from
the Santolini method, or using various network topological
properties including, but not limited to, clustering, proximity and
diffusion-based methods) are provided to a probabilistic neural
network to thereby provide (i.e., "train") the gene expression
response signature. In some embodiments, the probabilistic neural
network implements the algorithm proposed by D. F. Specht in
"Probabilistic Neural Networks," Neural Networks, 3(1):109-118
(1990), which is incorporated herein by reference. In some
embodiments, the probabilistic neural network is written in the
R-statistical language, and knowing a set of observations described
by a vector of quantitative variables, classifies observations into
a given number of groups (e.g., responders and non-responders). The
algorithm is trained with the data set of signature genes taken
from known responders and non-responders and guesses new
observations that are provided. In some embodiments, the
probabilistic neural network is one derived from
CRAN.R-project.org.
[0067] Alternatively or additionally, in some embodiments, a gene
expression response signature can be trained in the probabilistic
neural network using a cohort of known responders and
non-responders using leave-one-out cross and/or k-fold cross
validation. In some embodiments, such a process leaves one sample
out (i.e., leave-one-out) of the analysis and trains the classifier
only based on the remaining samples. In some embodiments, the
updated classifier is then used to predict a probability of
response for the sample that's left out. In some embodiments, such
a process can be repeated iteratively, for example, until all
samples have been left out once. In some embodiments, such a
process randomly partitions a cohort of known responders and
non-responders into k equal sizes groups. Of the k groups, a single
group is retained as validation data for testing the model, and the
remaining groups are used as training data. Such a process can be
repeated k times, with each of the k groups being used exactly once
as the validation data. In some embodiments, the outcome is a
probability score for each sample in the training set. Such
probability scores can correlate with actual response outcome. A
Recursive Operating Curves (ROC) can be used to estimate the
performance of the classifier. In some embodiments, an Area Under
Curve (AUC) of about 0.6 or higher reflects a suitable validated
classifier. In some embodiments, a Negative Predictive Value (NPV)
of 0.9 reflects a suitable validated classifier. In some
embodiments, a classifier can be tested in a completely independent
(i.e., blinded) cohort to, for example, confirm the suitability
(i.e., using leave-one-out and/or k-fold cross validation).
Accordingly, in some embodiments, provided methods further comprise
one or more steps of validating a gene expression response
signature, for example, by assigning probability of response to a
group of known responders and non-responders; and checking the gene
expression response signature against a blinded group of responders
and non-responders. The output of these processes is a trained gene
expression response signature useful for establishing whether a
subject will or will not respond to a particular therapy (e.g.,
anti-TNF therapy).
[0068] Accordingly, in some embodiments, the gene expression
response signature is established to distinguish between responsive
and non-responsive prior subjects who have received a type of
therapy, e.g., anti-TNF therapy. This gene expression response
signature, derived from these prior responders and non-responders,
is used to classify subjects (outside of the previously-identify
cohorts) as responders or non-responders, i.e., can predict whether
a subject will or will not respond to a given therapy. In some
embodiments, the response and non-responsive prior subjects
suffered from the same disease, disorder, or condition.
Detecting Classifier(s)
[0069] Detecting gene classifiers in subjects, once the gene
classifier is identified, is a routine matter for those of skill in
the art. In other words, by first defining the gene classifier, a
variety of methods can be used to determine whether a subject or
group of subjects express the established gene classifier. For
example, in some embodiments, a practitioner can obtain a blood or
tissue sample from the subject prior to administering of therapy,
and extract and analyze mRNA profiles from said blood or tissue
sample. The analysis of mRNA profiles can be performed by any
method known to those of skill in the art, including, but not
limited gene arrays, RNA-sequencing, nanostring sequencing,
real-time quantitative reverse transcription PCR (qRT-PCR), bead
arrays, or enzyme-linked immunosorbent assay (ELISA). Accordingly,
in some embodiments, the present disclosure provides methods of
determining whether a subject is classified as a responder or
non-responder, comprising measuring gene expression by at least one
of a microarray, RNA sequencing, real-time quantitative reverse
transcription PCR (qRT-PCR), bead array, and ELISA. In some
embodiments, the present disclosure provides methods of determining
whether a subject is classified as a responder or non-responder
comprising measuring gene expression of a subject by RNA sequencing
(i.e., RNAseq).
[0070] In some embodiments, the provided technologies provide
methods comprising determining, prior to administering anti-TNF
therapy, that a subject displays a gene expression response
signature associated with response to anti-TNF therapy; and
administering the anti-TNF therapy to the subject determined to
display the gene expression response signature. In some
embodiments, the provided technologies provide methods comprising
determining, prior to administering anti-TNF therapy, that a
subject does not display the gene expression response signature;
and administering a therapy alternative to anti-TNF therapy to the
subject determine not to display the gene expression signature.
[0071] In some embodiments, the therapy alternative to anti-TNF
therapy is selected from rituximab (Rituxan.RTM.), sarilumab
(Kevzara.RTM.), tofacitinib citrate (Xeljanz.RTM.), lefunomide
(Arava.RTM.), vedolizumab (Entyvio.RTM.), tocilizumab
(Actemra.RTM.), anakinra (Kineret.RTM.), and abatacept
(Orencia.RTM.).
[0072] In some embodiments, gene expression is measured by
subtracting background data, correcting for batch effects, and
dividing by mean expression of housekeeping genes. See Eisenberg
& Levanon, "Human housekeeping genes, revisited," Trends in
Genetics, 29(10):569-574 (October 2013). In the context of
microarray data analysis, background subtraction refers to
subtracting the average fluorescent signal arising from probe
features on a chip not complimentary to any mRNA sequence, i.e.
signals that arise from non-specific binding, from the fluorescence
signal intensity of each probe feature. The background subtraction
can be performed with different software packages, such as
Affymetrix.TM. Gene Expression Console. Housekeeping genes are
involved in basic cell maintenance and, therefore, are expected to
maintain constant expression levels in all cells and conditions.
The expression level of genes of interest, i.e., those in the
response signature, can be normalized by dividing the expression
level by the average expression level across a group of selected
housekeeping genes. This housekeeping gene normalization procedure
calibrates the gene expression level for experimental variability.
Further, normalization methods such as robust multi-array average
("RMA") correct for variability across different batches of
microarrays, are available in R packages recommended by either
Illumina.TM. and/or Affymetrix.TM. platforms. The normalized data
is log transformed, and probes with low detection rates across
samples are removed. Furthermore, probes with no available genes
symbol or Entrez ID are removed from the analysis.
[0073] In some embodiments, the present disclosure provides a kit
comprising a gene expression response signature established to
distinguish between responsive and non-responsive prior subjects
who have received anti-TNF therapy. In some embodiments, the kit
compares levels of gene expression of a subject to the gene
expression response signature (i.e., the gene classifier)
established to distinguish between responsive and non-responsive
prior subjects who have received anti-TNF therapy.
Using Classifiers
[0074] Patient Stratification
[0075] Among other things, the present disclosure provides
technologies for predicting responsiveness to anti-TNF therapies.
In some embodiments, provided technologies exhibit consistency
and/or accuracy across cohorts superior to previous
methodologies.
[0076] Thus, the present disclosure provides technologies for
patient stratification, defining and/or distinguishing between
responder and non-responder populations. For example, in some
embodiments, the present disclosure provides methods for treating
subjects with anti-TNF therapy, which methods, in some embodiments,
comprise a step of: administering the anti-TNF therapy to subjects
who have been determined to display a gene expression response
signature established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy. In some such embodiments, the gene expression response
signature includes a plurality of genes established to distinguish
between responsive and non-responsive prior subjects for a given
anti-TNF therapy. In some embodiments, the plurality of genes are
determined to cluster with one another in a human interactome map.
In some embodiments, the plurality of genes are proximal in a human
interactome map. In some embodiments, the plurality of genes
comprise genes that are shown to be statistically significantly
different between responsive and non-responsive prior subjects.
[0077] Therapy Monitoring
[0078] Further, the present disclosure provides technologies for
monitoring therapy for a given subject or cohort of subjects. As a
subject's gene expression level can change over time, it may, in
some instances, be necessary or desirable to evaluate a subject at
one or more points in time, for example, at specified and or
periodic intervals.
[0079] In some embodiments, repeated monitoring under time permits
or achieves detection of one or more changes in a subject's gene
expression profile or characteristics that may impact ongoing
treatment regimens. In some embodiments, a change is detected in
response to which particular therapy administered to the subject is
continued, is altered, or is suspended. In some embodiments,
therapy may be altered, for example, by increasing or decreasing
frequency and/or amount of administration of one or more agents or
treatments with which the subject is already being treated.
Alternatively or additionally, in some embodiments, therapy may be
altered by addition of therapy with one or more new agents or
treatments. In some embodiments, therapy may be altered by
suspension or cessation of one or more particular agents or
treatments.
[0080] To give but one example, if a subject is initially
classified as responsive (because the subject's gene expression
correlated to a gene expression response signature associated with
a disease, disorder, or condition), a given anti-TNF therapy can
then be administered. At a given interval (e.g., every six months,
every year, etc.), the subject can be tested again to ensure that
they still qualify as "responsive" to a given anti-TNF therapy. In
the event the gene expression levels for a given subject change
over time, and the subject no longer expresses genes associated
with the gene expression response signature, or now expresses genes
associated with non-responsiveness, the subject's therapy can be
altered to suit the change in gene expression.
[0081] Accordingly, in some embodiments, the present disclosure
provides methods of administering therapy to a subject previously
determined to display a gene expression response signature
associated with anti-TNF therapy, wherein the subject displays a
gene expression response signature associated with response to
anti-TNF therapy.
[0082] In some embodiments, the present disclosure provides methods
of treating subjects with anti-TNF therapy, the method comprising a
step of: administering the anti-TNF therapy to subjects who have
been determined to display a gene expression response signature
established to distinguish between responsive and non-responsive
prior subjects who have received the anti-TNF therapy.
[0083] In some embodiments, the present disclosure provides methods
further comprising determining, prior to the administering, that a
subject displays the gene expression response signature; and
administering the anti-TNF therapy to the subject determined to
display the gene expression response signature.
[0084] In some embodiments, the present disclosure provides methods
further comprising determining, prior to the administering, that a
subject does not display the gene expression response signature;
and administering a therapy alternative to anti-TNF therapy to the
subject determined not to display the gene expression response
signature.
[0085] In some embodiments, the gene expression response signature
was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to cluster with one
another in a human interactome map, thereby establishing the gene
expression response signature.
[0086] In some embodiments, the gene expression response signature
was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to be proximal with
one another in a human interactome map, thereby establishing the
gene expression response signature.
[0087] In some embodiments, the present disclosure provides methods
further comprising steps of: validating the gene expression
response signature by assigning probability of response to a group
of known responders and non-responders; and checking the gene
expression response signature against a blinded group of responders
and non-responders.
[0088] In some embodiments, the responsive and non-responsive prior
subjects suffered from the same disease, disorder, or
condition.
[0089] In some embodiments, the subjects to whom the anti-TNF
therapy is administered are suffering from the same disease,
disorder or condition as the prior responsive and non-responsive
prior subjects.
[0090] In some embodiments, the gene expression response signature
includes expression levels of a plurality of genes derived from a
cluster of genes associated with response to anti-TNF therapy on a
human interactome map.
[0091] In some embodiments, the gene expression response signature
includes expression levels of a plurality of genes proximal to
genes associated with response to anti-TNF therapy on a human
interactome map.
[0092] In some embodiments, the gene expression response signature
includes expression levels of a plurality of genes determined to
cluster with one another in a human interactome map.
[0093] In some embodiments, the gene expression response signature
includes expression levels of a plurality of genes that are
proximal in a human interactome map.
[0094] In some embodiments, genes of the subject are measured by at
least one of a microarray, RNA sequencing, real-time quantitative
reverse transcription PCR (qRT-PCR), bead array, ELISA, and protein
expression.
[0095] In some embodiments, the subject suffers from a disease,
disorder, or condition selected from rheumatoid arthritis,
psoriatic arthritis, ankylosing spondylitis, Crohn's disease,
ulcerative colitis, chronic psoriasis, hidradenitis suppurativa,
and juvenile idiopathic arthritis.
[0096] In some embodiments, the anti-TNF therapy is or comprises
administration of infliximab, adalimumab, etanercept, cirtolizumab
pegol, goliluma, or biosimilars thereof. In some embodiments, the
anti-TNF therapy is or comprises administration of infliximab or
adalimumab.
[0097] In some embodiments, the present disclosure provides, in a
method of administering anti-TNF therapy, the improvement that
comprises administering the therapy selectively to subjects who
have been determined to display a gene expression response
signature established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy.
[0098] In some embodiments, the responsive and non-responsive prior
subjects suffered from the same disease, disorder, or
condition.
[0099] In some embodiments, the subjects to whom the anti-TNF
therapy is administered are suffering from the same disease,
disorder or condition as the prior responsive and non-responsive
prior subjects.
[0100] In some embodiments, the gene expression response signature
includes expression levels of a plurality of genes derived from a
cluster of genes associated with response to anti-TNF therapy on a
human interactome map.
[0101] In some embodiments, the anti-TNF therapy is or comprises
administration of infliximab, adalimumab, etanercept, cirtolizumab
pegol, goliluma, or biosimilars thereof.
[0102] In some embodiments, the disease, disorder, or condition is
selected from rheumatoid arthritis, psoriatic arthritis, ankylosing
spondylitis, Crohn's disease, ulcerative colitis, chronic
psoriasis, hidradenitis suppurativa, and juvenile idiopathic
arthritis.
[0103] In some embodiments, the disease, disorder, or condition is
rheumatoid arthritis.
[0104] In some embodiments, the disease, disorder, or condition is
ulcerative colitis.
[0105] In some embodiments, the present disclosure provides use of
an anti-TNF therapy in the treatment of a subject determined to
display a gene expression response signature established to
distinguish between responsive and non-responsive prior subjects
who have received the anti-TNF therapy.
[0106] In some embodiments, prior to use of the anti-TNF therapy,
determining that the subject displays the gene expression response
signature. In some embodiments, prior to use of the anti-TNF
therapy, determining that the subject does not display the gene
expression response signature.
[0107] In some embodiments, the gene expression response signature
was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to cluster with one
another in a human interactome map, thereby establishing the gene
expression response signature.
[0108] In some embodiments, the gene expression response signature
was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by a method comprising steps of: mapping genes whose
expression levels significantly correlate to clinical
responsiveness or non-responsiveness to a human interactome map;
and selecting a plurality of genes determined to be proximal with
one another in a human interactome map, thereby establishing the
gene expression response signature.
[0109] In some embodiments, the gene expression response signature
was established to distinguish between responsive and
non-responsive prior subjects who have received the anti-TNF
therapy by the method further comprising steps of validating the
gene expression response signature by assigning probability of
response to a group of known responders and non-responders; and
checking the gene expression response signature against a blinded
group of responders and non-responders.
[0110] Therapy Reimbursement
[0111] Provided technologies also provide significant value to
regulatory agencies and payers (i.e., insurance companies). As
noted, in some embodiments, provided technologies provide
consistent and reliable means for predicting anti-TNF therapy
response for a given subject or cohort of subjects. As also noted,
for some anti-TNF therapies, response rate of subjects is only
about 65%. With a 35% chance that a given anti-TNF therapy may have
no or sub-optimal impact, or indeed a negative impact on a subject,
it is clearly beneficial for regulatory agencies and payers to be
able to reliably predict when a subject will or will not respond to
a given therapy.
[0112] Generally, when a patient is prescribed a particular
medication or therapy, the patient obtains or receives the
medication or therapy from a care provider, such as a pharmacy or a
medical practitioner. Commonly, the pharmacy or medical
practitioner pays for the medication outright, and then will either
pass the cost on to the patient (if coverage from an insurance
company/payer is not available), or will seek reimbursement for the
medication from the insurance company payer. If the medication is
covered, the payer reimburses the pharmacy or medical practitioner,
and the patient either pays nothing, or a co-pay. This system
operates under the assumption that the medication will be
beneficial to the patient. But, failure of a patient to respond to
a given therapy results in considerable wasted expense (not to
mention wasted time on the part of the patient), coupled with a
chance that the patient's disease, disorder, or condition could
progress, or that the patient may be subject to a side effect
associated with a given anti-TNF therapy. Either scenario results
in additional costs to the payer, as the payer has already
reimbursed the pharmacy or medical practitioner for the therapy.
These costs, however, could have been avoided in the first instance
with proper analysis of the patient prior to administering the
drug. Accordingly, the present disclosure provides technologies for
reimbursement of medical expenses relating to anti-TNF therapy.
[0113] Whether a payer will cover a given therapy is determined by
a committee referred to as the "Pharmacy and Therapeutics
Committee" (the "P&T Committee"). The P&T Committee is
responsible for managing the "formulary" of the agency. The
formulary is a continually updated list of medications and related
information, and includes a list of medications and
medication-associated products or devices, medication use policies,
important ancillary drug information, decision support tools, and
organization guidelines for an agency or a payer. The P&T
Committee, continually reviewing and revising the formulary,
establishes policies regarding the use of drugs, therapies, and
drug-related products, and identifies those that are most medically
appropriate and cost-effective. Part of the review of the formulary
is the pharmacoeconomic assessment, which includes
cost-minimization to the patient and payer, but also consideration
of nonmedication-related costs and financial consequences of the
pharmacy and the organization as a whole. Therefore, relevant to
the instant disclosure, it is the job of the P&T Committee to
determine whether certain diagnostic methods should be required or
associated with a particular medication. By requiring that a
subject obtain certain diagnostic information prior to
administration of a particular therapy, the payer can ensure that
the subject will indeed respond, and that no costs are wasted
reimbursing a subject for therapy that did not work.
[0114] Accordingly, the P&T Committee can require that either
prior authorization or step therapy be required before dispensing
specific medications. Prior authorization requires the prescriber
(i.e., the physician) to receive pre-approval for prescribing a
particular medication in order for that medication to qualify for
coverage. Drugs that require prior authorization will not be
approved for payment until the conditions for approval of the drug
are met. Prior authorization is useful because it can address the
need to obtain additional clinical patient information. Examples of
such information include clinical diagnosis, height, weight,
laboratory results, previous medications, and non-drug therapies
related to a specific disease, disorder, or condition. Within the
instant disclosure, a payer can require a prescriber receive prior
authorization for administering particular anti-TNF therapies. For
example, the payer can require that the patient prove that they
have the particular gene expression associated with the gene
expression response signature described herein (i.e., is a
responder) prior to authorizing payment of a particular anti-TNF
therapy. The payer can also deny coverage of a particular anti-TNF
therapy if the patient is classified as a non-responder (i.e., does
not express genes associated with the gene expression response
signature). Requiring such prior authorization allows payers to
avoid loss by reimbursing (covering) particular medications that
may not be therapeutically effective for a given subject.
[0115] Further, similar to prior authorization, some payers may
require that patients undergo step therapy prior to administering a
particular drug. Step therapy is the requirement that a patient
undergo a particular, approved, treatment prior to authorization of
the requested drug. For example, for patients suffering from
arthritis, a payer may require that a patient undergo therapy with
non-steroidal anti-inflammatory drugs (NSAIDs) prior to
administration of a different medication. Similarly, for example, a
payer can require that a patient be classified as a responder or a
non-responder based on the gene expression response signatures
described generally herein. For example, if a patient is classified
as a "non-responder" to a given anti-TNF therapy, the payer can
require that the patient undergo alternative therapy prior to
covering the given anti-TNF therapy. Utilizing this strategy allows
for the payer to ensure that other venues of treatment are
attempted before taking on the risk of paying for medication that
is unlikely to have any effect for a given patient.
[0116] Accordingly, in some embodiments, the present disclosure
provides technologies for reimbursement of medical expenses related
to anti-TNF therapy. In some embodiments, the present disclosure
provides technologies for adjudicating and reimbursing a care
provider for anti-TNF therapy, such methods comprising: receiving a
transaction from the care provider requesting reimbursement for the
anti-TNF therapy; aggregating data for the anti-TNF therapy from a
formulary; determining whether satisfying prior authorization
conditions is required for the anti-TNF therapy; determining an
amount of reimbursement using data aggregated from the formulary
and whether the prior authorization conditions are satisfied.
[0117] In some embodiments, the formulary requires the prior
authorization conditions are satisfied. In some embodiments, the
prior authorization conditions comprise determining whether a
subject displays a gene expression response signature established
to distinguish between responsive and non-responsive prior subjects
who have received the anti-TNF therapy.
[0118] In some embodiments, the subject satisfies the prior
authorization conditions by expressing the gene expression response
signature. In some embodiments, the care provider is reimbursed in
an amount between 1% and 100% of the cost of the anti-TNF
therapy.
[0119] In some embodiments, the subject does not satisfy the prior
authorization conditions by not expressing the gene expression
response signature. In some embodiments, the amount of
reimbursement is 0%.
[0120] In some embodiments, the present disclosure provides a
method of reimbursing medical expenses associated with
administration of anti-TNF therapy to a subject, the method
comprising: providing reimbursement if the subject had been
determined to display a gene expression response signature
established to distinguish between responsive and non-responsive
prior subjects who have received the anti-TNF therapy; and not
providing reimbursement if the subject had not been determined to
display the gene expression response signature.
EXEMPLIFICATION
[0121] Examples below demonstrate gene expression response
signatures (otherwise referred to as "classifiers" below)
characteristic of subjects who do or do not respond to anti-TNF
therapy.
Example 1: Determining Responder and Non-Responder Patient
Populations--Ulcerative Colitis
[0122] In accordance with the present disclosure, gene expression
data from subjects diagnosed with ulcerative colitis (UC) who had
received anti-TNF therapy was used to determine patients who are
responders and non-responders to anti-TNF therapy. This UC cohort
(GSE12251) included 23 patients diagnosed with UC, 11 of which did
not respond to anti-TNF-therapy. The gene expression data for this
cohort were generated using the Affymetrix.TM. platform.
[0123] The gene expression data was analyzed define a set of genes
(response signature genes) whose expression patterns distinguish
responders and non-responders. To do this, genes with significant
gene expression deviations between responders and non-responders
were relied on. Unlike conventional differential expression methods
that look for high fold changes in gene expression between two
groups, the present disclosure provides the insight that small but
significant changes between two groups of patients should be
included. The present disclosure thus identifies the source of a
problem with conventional differential expression technologies.
[0124] Without wishing to be bound by any particular theory, the
present disclosure provides an insight that small but significant
differences impact responsiveness to therapy. Indeed, the present
disclosure notes that, given that patients in these cohorts are all
diagnosed with the same disease, they often may not manifest big
FCs across genes. The present disclosure demonstrates that even
very small but significant changes in gene expression will lead to
a different treatment outcome.
[0125] Additionally, the present disclosure demonstrates that
analysis of genes displaying small (but significant) expression
differences, in context of a human "interactome" map, defines
signatures that reliably distinguish responders from
non-responders.
In-Cohort Analysis
[0126] Using a human interactome ("HI") map of gene connectivity
that reveals features of underlying biology of response and is
useful for understanding response signature genes.
[0127] The top 200 genes (as measured by p-value from lowest to
highest) whose expression values across patients were significantly
correlated to clinical outcome after treatment were selected and
mapped to HI. It was observed that even though these genes have
been found using the gene expression data only, they form a
significant cluster (module) on the HI, with the large connected
component ("LCC," i.e., classifier genes) being much bigger that
what is expected by chance HI (FIGS. 1A and 1B). Existence of such
significant modules (z-score>1.6) has been repeatedly shown to
be associated with underlying disease biology. See Barabasi, et
al., "Network medicine: a network-based approach to human disease,"
Nat. Rev. Genet, 12(1):56-68 (January 2011); Hall et al, "Genetics
and the placebo effect: the placebome," Trends Mol. Med.,
21(5):285-294 (May 2015); del Sol, et al., "Diseases as network
perturbations," Curr. Opin. Biotechnol., 21(4):566-571 (August
2010).
[0128] FIGS. 1A and 1B show the subnetwork containing the genes
correlated to phenotypic outcome in UC cohort as well as their
interactions. A significant number of genes found by gene
expression analysis form the LCC of the subgraph. The LCC genes
(classifier genes) were then utilized to feed and train a
probabilistic neural network. The result of the analysis shows a
near perfect classifier with an Area Under the Curve (AUC) of 0.98
and with 100% accuracy in predicting non-responders.
[0129] The performance of trained classifiers was validated using a
leave-one-out cross validation approach. FIGS. 2A and 2B show the
receiver operator curves (ROC) as well as negative prediction power
(predicting non-responders) of the classifier. The classifier is
able to detect 70% of the non-responders within a cohort.
TABLE-US-00002 TABLE 2 No. Genes No. Genes Cohort ID Selected in HI
LCC Size Significance GSE12251 200 193 41 2.33
[0130] Table 2 represents the number and topological properties
(i.e., the size of the largest component on the network and its
significance) of response signature genes when mapped onto the
network.
[0131] A known and major drawback of traditional gene expression
analysis is the inability to reproduce the results across different
studies. See Ioannidis J. P. A., "Why most published research
findings are false," PLoS Med. 2(8):e124 (2005); Goodman S. N., et
al., "What does research reproducibility mean?" Sci. Transl. Med.,
8(341):341-353 (2016); Ioannidis J. P., et al. "Replication
validity of genetic association studies." Nat. Genet. 29:(3)306-309
(November 2001). Below, it is shown that the methods and systems
described herein are able to make high accuracy predictions across
cohorts. To estimate the power of the classifier, the classifier is
tested in a completely independent cohort (GSE14580) and in a
blinded fashion. The independent UC cohort includes 16
non-responders and 8 responders.
[0132] For cross-platform validation, the two cohorts were merged
and batch effects removed using the R package, ComBat, a tool used
for batch-adjusting gene expression data. See Johnson W. E., et
al., "Adjusting batch effects in microarray expression data using
empirical Bayes methods," Biostatistics 8(1), 118-127 (2007). The
performance of the designed classifier was tested in the
independent cohort (leave-one-batch-out cross validation). FIGS. 3A
and 3B show the ROC and negative prediction curves associated with
cross-cohort performance of the designed classifier. The trained
classifier shows significantly high performance in the independent
cohort with AUC of 0.78.
[0133] Aside from the high cross-cohort performance assessed by
AUC, cross-cohort NPV (Negative Predictive Value) and TNR (True
Negative Rate), which indicates the accuracy of detecting
non-responders in a blind cohort, were also estimated (FIG. 3B).
The cross-cohort validation shows that the classifier is able to
predict at least 50% of non-responders (NPV=1, TNR=0.5). The
classifier is able to detect more non-responders (TNR>0.5),
which results in slight drop in NPV (FIG. 3B). Nevertheless,
regardless of the selected point on the curve, the classifier meets
or exceeds the commercial criteria (NPV of 0.9 and TNR of 0.5) set
by health insurance companies.
Disease Biology of Non-Responders
[0134] The network defined by the analysis described herein
provides insights into underlying biology of this response
prediction. The classifier genes within the response module were
analyzed using GO terms to identify the most highly enriched
pathways. We found that inflammatory signaling pathways (including
TNF signaling) were highly enriched, as were pathways linked to
sumoylation, ubiquitination, proteasome function, proteolytic
degradation and antigen presentation in immune cells. Thus, the
network approach described herein has captured protein interactions
for selecting genes within the response module that clearly reflect
the biology of the disease and drug response at the independent
patient level and allow the accurate prediction of response to
anti-TNF therapies from a baseline sample.
Discussion
[0135] A known and significant problem with existing anti-TNF
therapy approaches is that "many patients do not respond to the . .
. therapy (primary non-response--PNR), or lose response during the
treatment (secondary loss of response--LOR)." See, e.g., Roda et
al., Clin Gastroentorl. 7:e135, January 2016. Specifically, reports
indicate that "around 10-30% of patients do not respond to the
initial treatment and 23-46% of patients lose response over time"
Id. Thus, overall, the drug response rate for anti-TNF therapy (and
in particular for anti-TNF therapy to treat UC patients) is below
65%, resulting in continued disease progression and escalating
treatment costs for the majority of the patient population.
Moreover, billions of dollars are spent prescribing anti-TNF
therapies to patients that don't respond. There is a significant
need for development of a technology that can identify responder vs
non-responder subjects, prior to initiation of therapy, at the time
that therapy (e.g., a particular dose) is administered, and/or over
time as therapy has been or is received.
[0136] Gene expression data has been touted as holding the promise
of being able to uncover disease biology of individual patients in
complex diseases, but up until now the data has been difficult to
interpret, and efforts to develop biomarkers (e.g., expression
signatures) for therapeutic responsiveness have failed in
cross-cohort validation tests. The present disclosure provides new
technologies that, for example, consider relatively small changes
in expression levels and/or participation of genes in relevant
parts of the human interactome.
[0137] As already noted, the present disclosure demonstrates that
projecting baseline gene expression profiles from UC patients that
are non-responders to anti-TNF therapy on the HI, reveals that such
profiles cluster and form a large connected module that describes
the non-responders' disease biology. In accordance with the present
invention, a classifier developed from genes expressed in this
module predicts non-response with a high level of accuracy and has
been validated in a completely independent cohort (cross-cohort
validation). Furthermore, this classifier meets the commercial
criteria set by insurance companies and is therefore ready for
clinical development and future commercialization.
Methods
[0138] Microarray Analysis
[0139] Cohort 1, GSE14580: Twenty-four patients with active UC,
refractory to corticosteroids and/or immunosuppression, underwent
colonoscopy with biopsies from diseased colon within a week prior
to the first intravenous infusion of 5 mg infliximab per kg body
weight. Response to infliximab was defined as endoscopic and
histologic healing at 4-6 weeks after first infliximab treatment
using the MAYO score. Six control patients with normal colonoscopy
were included. Total RNA was isolated from colonic mucosal
biopsies, labelled and hybridized to Affymetrix.TM. Human Genome
U133 Plus 2.0 Arrays.
[0140] Cohort 2, GSE12251: Twenty-two patients underwent
colonoscopy with biopsy before infliximab treatment. Response to
infliximab was defined as endoscopic and histologic healing at week
8 using the MAYO score (P2, 5, 9, 10, 14, 15, 16, 17, 24, 27, 36,
and 45 as responders; P3, 12, 13, 19, 28, 29, 32, 33, 34, and 47 as
non-responders). Messenger RNA was isolated from pre-infliximab
biopsies, labeled and hybridized to Affymetrix.TM. HGU133 Plus_2.0
Array.
[0141] Identification of Classifier Genes
[0142] Genes with expression values across patients that were
significantly correlated to clinical measures after treatment were
selected as best determinants of response. These genes were mapped
on the consolidated Human Interactome ("HI"). The consolidated
Human Interactome collects physical protein interactions between a
cell's molecular components relying on experimental support. The
material reported by Barabasi et al. in "Uncovering disease-disease
relationships through the incomplete interactome," Science,
347(6224):1257601 (February 2015), the entirety of which is
incorporated herein by reference, provides instruction regarding
how to build and curate a Human Interactome. The genes on the Human
Interactome are not randomly scattered on the network. Instead,
they significantly interact with each other, reflecting the
existence of an underlying disease biology module that explains
response.
[0143] Human Interactome
[0144] As noted, the HI contains experimentally supported physical
interactions between cellular components. These interactions were
queried from several resources but only selected those that are
supported by experimental validation. Most of the interactions in
the HI are from unbiased high-throughput studies such as Y2H. All
included data were experimentally supported interactions that have
been reported in at least two publications. These interactions
include, regulatory, metabolic, signaling and binary interactions.
The HI contains about 17k cellular components and over 200K
interactions among them. Unlike other interaction databases, no
computationally inferred interaction were included, nor any
interaction curated from text parsing of literature with no
experimental validation.
[0145] Classifier Design and Validation
[0146] Genes identified above were used as features of a
probabilistic neural network. The classifier was validated using
leave-one-out and/or k-fold cross validation within a given cohort.
The classifier was trained based on the outcome data provided on
all patients but the one left out. The classifier was blind to the
response outcome of that left out patient. Predicting the outcome
of the patient that has been left out then validated the trained
classifier. This procedure was repeated so that each patient was
left out once. The classifier provided a probability for each
patient reflecting whether they belong to responder or
non-responder group. The logarithm of likelihood ratio was used to
assign a score to each patient. Patients were then ranked based on
their score and prediction accuracy values were estimated by
varying the classifier threshold resulting in the ROC curves. In
particular, each patient is given a score by the trained
classifier. The prediction accuracy is measured for the entire
cohort as a whole and by checking whether given scores across
patients well distinguish responders and non-responders. Prediction
performance is generally measured by the Area Under the Curve
(AUC). When higher levels of accuracy are required, negative
predictive value (NPV) and true negative rate (TNR) can be used.
The score cutoff that results in best group separation (e.g.,
highest NPV) is set for future predictions.
Example 2: Determining Responder and Non-Responder Patient
Populations--Rheumatoid Arthritis
[0147] Analogous to Example 1, the present Example 2 describes
prediction of response and/or non-response to anti-TNF therapy in
patients suffering from rheumatoid arthritis (RA). The presently
described predictions satisfy the performance threshold identified
by payers and physicians of Negative Predictive Value (NPV) of 0.9
and True Negative Rate (TNR) of 0.5.
[0148] In the present example, gene expression data from baseline
blood samples for two cohorts comprising a total of 89 RA patients
were analyzed. The methodology utilized in the present Example to
develop a classifier (i.e., a gene expression response signature)
that predicted response and/or non-response to anti-TNF therapy
included a four step process. First, initial genes were selected
based on differential expression between responders and
non-responders to anti-TNF therapy. Second, such genes were
projected on the human interactome to determine which genes form a
significant and biologically relevant cluster. Third, genes that
cluster on the interactome were selected and fed into a
probabilistic neural network (PNN) to develop the final
classifiers. And fourth, each classifier was validated using
leave-one-out validation in the training set, and validated
cross-cohort in an independent cohort of patients (test set). For
RA, the final classifier contained 9 genes and reached an NPV of
0.91 and TNR of 0.67 in the test set.
[0149] The developed classifiers meet the performance thresholds
set by payers and physicians; those skilled in the art will
appreciate that these classifiers are useful tests that predict
non-response to anti-TNFs prior to initiation of therapy and/or to
assess desirability of altering administered therapy. Among other
things, provided technology therefore permits selection of therapy
(whether initial therapy or continued or altered therapy),
including enabling patients to be switched onto alternative
therapies faster, resulting in substantial clinical benefits to
patients and savings to the healthcare system.
Data Description
[0150] The response prediction analysis in RA utilized in the
present Example was based on two individual cohorts (Tables 3 and
4). Response was measured 14-weeks after initiation of anti-TNF
therapy, with response rates (Good responders; DAS28
improvement>1.2, corresponding to LDA or remission) in cohort 1
and 2 of 30% and 23%, respectively. Cohort 1 was used to train the
classifier and cohort 2 was used as the independent test cohort to
validate the predictive power of the classifier.
[0151] The analyses were conducted on RNA expression data generated
from whole blood, before initiation of therapy, using an
Illumina.TM. BeadArray.TM. platform and provided as standard output
of BeadStudio.TM.. Raw data was normalized and processed using lumi
package in R.
TABLE-US-00003 TABLE 3 Clinical response according to EULAR DAS28
criteria Cohort 1 Cohort 2 No. Good responders 15 9 No. Moderate
responders 15 15 No. Non-responders 20 15
TABLE-US-00004 TABLE 4 DAS28 Improvement Baseline DAS28 >1.2
>0.6 & .ltoreq.1.2 .ltoreq.0.6 .ltoreq.3.2 Good Moderate No
response response response >3.2 & .ltoreq.5.1 Moderate
Moderate No response response response >5.1 Moderate No response
No response response
Identifying Classifier Genes
[0152] Expression values for over 10,000 probes (genes) were
available in each patient; those skilled in the art will appreciate
the challenges associated with defining a set of genes (features)
that effectively distinguishes response from such a volume of data.
Insights provided by the present disclosure, including that
particularly useful genes for inclusion in a classifier may, in
some embodiments, be those with relatively small changes, permit
effective selection of gene (feature) set(s) for use in a
classifier.
[0153] In the present Example, genes for inclusion in an RA
classifier were selected via a multi-step analysis: First, genes
were ranked based on their significance of correlation to patient's
response outcome (change in baseline DAS28 score at week 14) using
Pearson correlation resulting in 200 top ranked genes (Feature set
1). Unlike conventional differential expression methods that look
for highest fold changes in gene expression between two groups, the
present Example captures small but significant changes between two
groups of patients.
[0154] Second, the present disclosure appreciates that gene
products (proteins) do not function in isolation, and furthermore
appreciates that reference to the interactome--a map of protein
interconnectivity--can valuably be used as a blueprint to
understand roles played by individual gene products in context
(i.e., in biology of cells and/or organisms). By mapping the 200
genes identified above on the interactome, a significant cluster,
or response module, consisting of 41 proteins was identified (Table
5). Existence of a significant cluster was repeatedly shown to be
associated with underlying disease biology. The observed response
module not only uncovers the underlying biology of response but
also served as Features set 2. In particular, FIG. 6 illustrates a
classifier development flowchart containing identifying features of
the classifier (A), training and validation of a probabilistic
neural network on cohort 1 using identified features (B) and
validation of the trained classifier using identified feature genes
expressions in an independent cohort (C). The final set of features
are selected based on best performance.
TABLE-US-00005 TABLE 5 #Top genes #Genes Cohort ID selected in HI
LCC size Significance 1 200 186 41 1.19
Training the Response Classifier and In-Cohort Validation
[0155] In the present Example, a response classifier was trained by
feeding a probabilistic neural network with Feature set 1 and 2.
Training the classifier on Feature set 1 significantly predicted
response using leave-one-out cross validation and reached an AUC of
0.69, an NPV of 0.9 and a TNR of 0.52 (FIG. 4A, and FIG. 4B,
respectively), outperforming Feature set 2. Having a smaller number
of classifier genes also opens up the opportunity to use a variety
of lower cost, FDA-approved expression platforms with a broad
installed base to generate the required gene expression data sets.
The classifier was therefore further trained to see if performance
holds up when reducing the number of genes in Feature set 1 by
training on top n-ranked genes where n goes from 1 to 20. A local
maximum was observed in classifier performance when training on the
top 9 genes (AUC=0.74, corrected p-value=0.006) with an NPV of 0.92
and a TNR of 0.76 (FIG. 4C and FIG. 4D). The 9-gene classifier was
chosen for the cross cohort validation analysis below.
Validation of Trained Response Classifier in an Independent Cohort
(Cross-Cohort Validation)
[0156] Of critical importance when building diagnostic tests and
classifiers is the ability to reproduce the results and
successfully test the classifier's performance in an independent
cohort. The developed 9-gene classifier was therefore tested in a
blinded fashion on a completely independent group of patients
(cohort 2). The results show that the classifier performed well
(cross-cohort AUC=0.78, p value=0.01) with an NPV of 0.91 and a TNR
of 0.67 (FIG. 5B and Table 6). FIG. 5A is an ROC curve of
cross-cohort classifier test results.
TABLE-US-00006 TABLE 6 Predicting non-responders for TNF-naive
patients AUC NPV TNR Classifier trained on cohort 1 tested on
cohort 2 0.78 0.91 0.67
Discussion
[0157] The present Example documents effectiveness of a classifier,
as described herein, that predicts non-response to anti-TNF drugs
before therapy is prescribed in patients suffering from RA.
[0158] Interviews with payers and clinicians indicate that current
target specifications aim to identify at least half of the
non-responders to anti-TNF therapy with high negative predictive
accuracy (NPV>90%). Patients that are identified as
non-responders can be placed on alternative effective therapies and
higher response rates for those patients still offered anti-TNFs
can be achieved. Financial savings are garnered by not spending on
expensive ineffectual therapies and avoiding serious side effects
and continuing disease progression. By identifying 50% of the
non-responders, significant cost and care benefits can be achieved
since, in the absence of stratification, two-thirds of patients do
not achieve the target of LDA or remission today. High NPV is
desired to ensure that few patients that would have responded are
not incorrectly withheld a therapy they would have benefited
from.
[0159] For RA, the present disclosure has demonstrated an AUC of
0.78, an NPV of 0.91 and a TNR of 0.67, resulting in the matrix
below (Table 7). That is, the classifier identifies 67% of true
non-responders with a 91% accuracy. Stratifying patients using this
classifier would increase the response rate for the anti-TNF
treated group by 71% from 34% to 58%. By comparison, the highest
cross-cohort performance reported for classifiers developed by
others had an NPV of 0.71 and a TNR of 0.71. See Toonen E J. et al.
"Validation study of existing gene expression signatures for
anti-TNF treatment in patients with rheumatoid arthritis." PLoS
One. 2012; 7(3):e33199. Using that classifier would significantly
misclassify the genuine responders leading to a worse overall
response rate than not using it at all. The presently described
classifiers clearly meet the performance targets when tested in an
independent cohort of patients.
TABLE-US-00007 TABLE 7 Predicted R NR Actual R 30 4 34 TPR 87% NR
22 44 66 TNR 67% 52 48 PPV 58% NPV 91%
[0160] The reduced number of genes in the classifier allows several
expression analysis platforms to be considered for the delivery of
the final commercial version of the test. For example, Nanostring
nCounter system uses digital barcode technology to count nucleic
acid analytes for panels of up to several hundred genes on an FDA
approved platform. Multiplexed qRT-PCR is the gold standard for
quantifying gene expression for panels of less than .about.20 genes
and would enable the test to be offered as a distributable kit. RA
is a chronic, complex autoimmune diseases, where many genetic risk
factors have been identified but none of them are of sufficient
impact to be useful as diagnostic or prognostic markers. The
present disclosure provides a ranked list of candidate genes based
on correlation of baseline expression level with response outcomes.
The rank order is derived from the significance of the correlation.
The present disclosure, however, does not prioritize genes with
larger fold change across the category of responders and
non-responders. It is common practice in the field to give
preference to genes that show the highest fold change. This is
because it is generally believed that large changes in expression
levels are biologically more meaningful, and because of the
technical advantage of high signal to noise ratios to compensate
for high background and other sources of technical variability.
However, the present disclosure appreciates that small differences,
which are ignored or overlooked in many conventional technologies,
can provide important, and even critical, discriminating
capability. Without wishing to be bound by any particular theory,
the present disclosure proposes that subtle differential
perturbations may be particularly relevant and/or important in
situations, like the present, where subjects suffering from the
same disease, disorder, or condition are compared with one another
(e.g., rather than with "control" subjects not suffering from the
disease, disorder, or condition). It may be that small yet
statistically significant differences in gene expression
differentiate patient populations in complex diseases such as RA.
This study shows that even very small but significant changes in
gene expression will lead to a different treatment outcome. This
method captures genes that are overlooked by conventional
differential expression analysis.
[0161] Additionally, the present disclosure utilizes the highly
unbiased and independently validated map of the protein-protein
interactions in cells, the human interactome. By mapping the
prioritized genes to the interactome, distinct and statistically
significant clusters appear. In addition to using the interactome
network analysis to define the classifier, the identified clusters
also provide biological insights into the biology and causal genes
of anti-TNF response. The genes corresponding to the top 9 genes in
RA are valuable in immunological pathways and functions linked to
ER stress, the protein quality control pathway, control of the cell
cycle and the ubiquitin proteasome system, primarily in targeting
key regulators of the cell cycle to the proteasome through
ubiquitinyation.
[0162] The classifiers described here serve as the basis for
diagnostic tests to predict anti-TNF non-response for patients with
moderate to severe disease and considering initiating biologic
therapy. Patients identified as non-responders will be offered
alternative, approved mechanism of action therapies. These tests
will provide significant improvements to current clinical practice
by increasing the proportion of patients reaching treatment goals,
making the treatment assignment based on scientific data and as a
result decrease waste of resources and generate significant
financial savings within the health care system.
Materials and Methods
[0163] RA Cohort Description and Microarray Analysis
[0164] Blood samples were collected from RA patients across the
United States from two individual observational studies, both of
which predominantly consisted of older Caucasian women. Cohort 1
was obtained from a multi-center study conducted in 2014. These
patients were treated with Enbrel, Remicade, Humira, Cimzia and
Simponi. Cohort 2 was obtained from the Autoimmune Biomarkers
Collaborative Network, a NIAMS supported contract to develop new
approaches to biomarkers for RA and lupus in 2003. These patients
were treated with Humira, Remicade and Enbrel.
[0165] The level of response was defined using the EULAR DAS28
scoring criteria assessed 14 weeks after anti-TNF treatment. EULAR
response rates for female TNF naive patients are given in Table 1.
EULAR response characterizes patients into good responders,
moderate responders and non-responders. For this study, response
was defined as EULAR good response, or DAS28 improvement>1.2.
This corresponds to LDA or remission.
[0166] The gene expression data and 14 week response outcome was
available for 50 and 39 female and TNF naive samples in cohort 1
and 2, respectively, for classifier design and validation.
[0167] All subjects had PaxGene.RTM. tubes drawn at baseline before
starting therapy, and again at 14 weeks after treatment started.
RNA was isolated using the QIAcube.RTM. (Qiagen.RTM.) following the
manufacturer's automated protocol for PaxGene.RTM. blood RNA.
Extracted samples were eluted in 80 ul of elution buffer (BR5) and
subsequently run on Agilent's 2100 Bioanalyzer.TM. of RNA integrity
using the RNA 6000 Nanochip. Samples with RNA Integrity Numbers
(RIN)>6.5 were diluted to 30 ng/.mu.l in a total 11 .mu.l of
RNAse-free water. Samples were amplified using Life Technologies
Illumina.TM. RNA Total Prep.TM. Amplification Kit. 750 ng of cRNA
was re-suspended in 5 .mu.l of RNAse-free water for analysis on the
Illumina.TM. Human HT-1.2v4 chip (cohort 1 samples) and 1.2 .mu.g
was re-suspended in 10 .mu.l of RNAse-free water for analysis
Illumina.TM. WG6v3 Bead Chip (cohort 2 samples). All samples were
processed according to the manufacturer's instructions.
[0168] Raw data were exported from GenomeStudio.TM. and further
analyzed with the R programming language. All datasets were
background corrected using the R/Bioconductor package "lumi." Data
were further transformed using variance stabilization
transformational (vst) and quantile normalized. Probes with zero
detection count and detection rates of lower that 50% across
samples were removed from the study. To enable cross cohort
classifier testing, the two cohorts were combined and normalized
using the ComBat package in R and then separated to ensure
completely blind testing. All of the microarray analysis resulted
in having about 10,000 common probes in the two cohorts.
[0169] Identification of Classifier Genes
[0170] Genes with expression values that are significantly
correlated to clinical measures after treatment are selected as the
best determinants of response. Expression correlation of gene
expression to response outcome is measured by Pearson correlation.
Genes are ranked based on the correlation value and the performance
of the classifier is assessed when using top n ranked genes. In
some cases mapping the ranked genes on the interactome forms a
significant cluster reflecting the underlying biology of response.
It is observed that the ranked genes are not randomly scattered on
the network. Instead, they significantly interact with each other,
reflecting the existence of an underlying disease biology module
that explains response.
[0171] Classifier Design and Validation
[0172] Genes identified in the previous step were used as features
of a probabilistic neural network. In this approach the average
distance of each sample to training samples' probability
distribution functions is calculated. The average distance of a
test sample to training samples in the n-dimensional feature space
determines the probability of belonging to one group vs. the other.
The classifier was validated using leave-one-out cross validation
within a given cohort. In this approach, the classifier was trained
based on the outcome data provided on all patients but the one left
out. The classifier was blind to the response outcome of that left
out patient. Predicting the outcome of the patient that has been
left out then validated the trained classifier. This procedure was
repeated so that each patient was left out once. The classifier
provided a probability for each patient reflecting whether they
responded or not. These probabilities were used to define a score
(by using log of likelihood ratio) for each patient. The area under
the curve (AUC) determined the performance of the classifier. In
cross-cohort assessment of the classifier, the trained classifier
was completely blind to the outcome of the independent cohort.
Trained data on one cohort is tested to determine its ability to
predict response in an independent cohort.
[0173] Statistical Analysis
[0174] Fisher's t-test was used to determine the significance of
difference between two distributions.
[0175] Human Interactome
[0176] The human interactome contains experimentally supported
physical interactions between cellular components. These
interactions are collected from several resources but only those
supported by a rigorous experimental validation confirming the
existence of a physical interaction between proteins are selected.
Most of the interactions in the interactome are from unbiased
high-throughput studies such as yeast 2-hybrid. Experimentally
supported interactions that that have been reported in at least two
publications are also included. These interactions include
regulatory, metabolic, signaling and binary interactions. The
interactome contains about 17,000 cellular components and over
200,000 interactions. Unlike other interaction databases the
present methods do not include any computationally inferred
interactions, nor any interaction curated from text parsing of
literature with no experimental validation. Therefore, the
interactome used is the most complete, carefully selected and
quality controlled version to date.
[0177] The foregoing has been a description of certain non-limiting
embodiments of the subject matter described within. Accordingly, it
is to be understood that the embodiments described in this
specification are merely illustrative of the subject matter
reported within. Reference to details of the illustrated
embodiments is not intended to limit the scope of the claims, which
themselves recite those features regarded as essential.
[0178] It is contemplated that systems and methods of the claimed
subject matter encompass variations and adaptations developed using
information from the embodiments described within. Adaptation,
modification, or both, of the systems and methods described within
may be performed by those of ordinary skill in the relevant
art.
[0179] Throughout the description, where systems are described as
having, including, or comprising specific components, or where
methods are described as having, including, or comprising specific
steps, it is contemplated that, additionally, there are systems
encompassed by the present subject matter that consist essentially
of, or consist of, the recited components, and that there are
methods encompassed by the present subject matter that consist
essentially of, or consist of, the recited processing steps.
[0180] It should be understood that the order of steps or order for
performing certain action is immaterial so long as any embodiment
of the subject matter described within remains operable. Moreover,
two or more steps or actions may be conducted simultaneously.
* * * * *